{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NREL WIND Toolkit - NSRDB Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook compares the National Renewable Energy Laboratory (NREL) Wind Integration National Dataset (WIND) Toolkit and National Solar Radiation Database (NSRDB) data. The data is provided from Amazon Web Services using the HDF Group's Highly Scalable Data Service (HSDS).\n",
    "\n",
    "Please consult the README file for setup instructions prior to running this notebook.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:49:32.976977Z",
     "start_time": "2019-02-07T02:49:31.938487Z"
    }
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "import h5pyd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import wilcoxon, ttest_rel\n",
    "from scipy.spatial import cKDTree\n",
    "from pyproj import Proj\n",
    "\n",
    "#custom plotting properties\n",
    "sns.set_style(\"white\")\n",
    "sns.set_style(\"ticks\")\n",
    "meanprops = dict(marker='o', markeredgecolor='black', markerfacecolor=\"None\",\n",
    "                 markersize=5)\n",
    "\n",
    "\n",
    "def WTK_idx(wtk, lat_lon):\n",
    "    \"\"\"\n",
    "    Function to find the nearest x/y WTK indices for a given lat/lon using Proj4 projection library\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    wtk : 'h5pyd.File'\n",
    "        h5pyd File instance for the WTK\n",
    "    lat_lon : tuple | list\n",
    "        (lat, lon) coordinates of interest\n",
    "        \n",
    "    Results\n",
    "    -------\n",
    "    ij : 'tuple'\n",
    "        x/y coordinate in the database of the closest pixel to coordinate of interest\n",
    "    \"\"\"\n",
    "    dset_coords = wtk['coordinates']\n",
    "    projstring = \"\"\"+proj=lcc +lat_1=30 +lat_2=60 \n",
    "                    +lat_0=38.47240422490422 +lon_0=-96.0 \n",
    "                    +x_0=0 +y_0=0 +ellps=sphere \n",
    "                    +units=m +no_defs \"\"\"\n",
    "    projectLcc = Proj(projstring)\n",
    "    origin_ll = reversed(dset_coords[0][0])  # Grab origin directly from database\n",
    "    origin = projectLcc(*origin_ll)\n",
    "    \n",
    "    lon_lat = reversed(lat_lon)\n",
    "    coords = projectLcc(*lon_lat)\n",
    "    delta = np.subtract(coords, origin)\n",
    "    ij = [int(round(x / 2000)) for x in delta]\n",
    "    return tuple(reversed(ij))\n",
    "\n",
    "\n",
    "def NSRDB_idx(nsrdb, lat_lon):\n",
    "    \"\"\"\n",
    "    Function to find the NSRDB site index for a given lat/lon using a KDTree\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    nsrdb : 'h5pyd.File'\n",
    "        h5pyd File instance for the NSRDB\n",
    "    lat_lon : tuple | list\n",
    "        (lat, lon) coordinates of interest\n",
    "        \n",
    "    Results\n",
    "    -------\n",
    "    ij : 'tuple'\n",
    "        x/y coordinate in the database of the closest pixel to coordinate of interest\n",
    "    \"\"\"\n",
    "    dset_coords = nsrdb['coordinates'][...]\n",
    "    tree = cKDTree(dset_coords)\n",
    "    dist, pos = tree.query(np.array(lat_lon))\n",
    "    return pos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare NSRDB to WIND Toolkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:49:32.981442Z",
     "start_time": "2019-02-07T02:49:32.979002Z"
    }
   },
   "outputs": [],
   "source": [
    "# Specify a latitude and longitude\n",
    "lat_lon = (32.785450, -96.800734) # Dallas, TX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NSRDB Site"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:49:49.651420Z",
     "start_time": "2019-02-07T02:49:32.983253Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NSRDB nearest site coordinates =  [ 32.77 -96.82]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>elevation</th>\n",
       "      <th>timezone</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>urban</th>\n",
       "      <th>population</th>\n",
       "      <th>landcover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.77</td>\n",
       "      <td>-96.82</td>\n",
       "      <td>130.199997</td>\n",
       "      <td>-6</td>\n",
       "      <td>b'United States'</td>\n",
       "      <td>b'Texas'</td>\n",
       "      <td>b'Dallas'</td>\n",
       "      <td>b'Dallas'</td>\n",
       "      <td>61419</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   latitude  longitude   elevation  timezone           country     state  \\\n",
       "0     32.77     -96.82  130.199997        -6  b'United States'  b'Texas'   \n",
       "\n",
       "      county      urban  population  landcover  \n",
       "0  b'Dallas'  b'Dallas'       61419        190  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find nearest NSRDB site to desired lat_lon\n",
    "with h5pyd.File('/nrel/nsrdb/v3/nsrdb_2017.h5') as nsrdb:\n",
    "    idx = NSRDB_idx(nsrdb, lat_lon)\n",
    "    site_meta = pd.DataFrame(nsrdb['meta'][[idx]])\n",
    "    nsrdb_lat_lon = nsrdb['coordinates'][idx]\n",
    "    print('NSRDB nearest site coordinates = ', nsrdb_lat_lon)\n",
    "    display(site_meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:51:01.136077Z",
     "start_time": "2019-02-07T02:49:49.655545Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ghi</th>\n",
       "      <th>dni</th>\n",
       "      <th>dhi</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>air_temperature</th>\n",
       "      <th>solar_zenith_angle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>96.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 01:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>108.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 02:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 03:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>132.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 04:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>145.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ghi  dni  dhi  wind_speed  air_temperature  \\\n",
       "datetime                                                          \n",
       "2007-01-01 00:00:00  0.0  0.0  0.0         1.9              4.0   \n",
       "2007-01-01 01:00:00  0.0  0.0  0.0         1.8              3.0   \n",
       "2007-01-01 02:00:00  0.0  0.0  0.0         1.9              2.0   \n",
       "2007-01-01 03:00:00  0.0  0.0  0.0         2.2              2.0   \n",
       "2007-01-01 04:00:00  0.0  0.0  0.0         2.6              2.0   \n",
       "\n",
       "                     solar_zenith_angle  \n",
       "datetime                                 \n",
       "2007-01-01 00:00:00               96.27  \n",
       "2007-01-01 01:00:00              108.13  \n",
       "2007-01-01 02:00:00              120.42  \n",
       "2007-01-01 03:00:00              132.95  \n",
       "2007-01-01 04:00:00              145.54  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract hourly irradiance, temperature, wind speed, and solar zenith angle from 2007-2013 NSRDB files\n",
    "nsrdb_data = []\n",
    "for year in range(2007, 2014):\n",
    "    file = '/nrel/nsrdb/v3/nsrdb_{}.h5'.format(year)\n",
    "    df = pd.DataFrame()\n",
    "    with h5pyd.File(file, 'r') as nsrdb:\n",
    "        for dset in ['ghi', 'dni', 'dhi', 'wind_speed', 'air_temperature', 'solar_zenith_angle']:\n",
    "            ds = nsrdb[dset]\n",
    "            df[dset] = ds[::2, idx] / ds.attrs['psm_scale_factor']\n",
    "        \n",
    "        df.index = pd.to_datetime(nsrdb['time_index'][::2].astype(str))\n",
    "        df.index.name = 'datetime'\n",
    "    \n",
    "    nsrdb_data.append(df)\n",
    "    \n",
    "nsrdb_data = pd.concat(nsrdb_data)\n",
    "nsrdb_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WIND Toolkit Site"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:51:03.045637Z",
     "start_time": "2019-02-07T02:51:01.138491Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WTK nearest site coordinates =  (32.789417, -96.789795)\n"
     ]
    }
   ],
   "source": [
    "# Find nearest WTK site to desired lat_lon\n",
    "with h5pyd.File(\"/nrel/wtk-us.h5\", 'r') as wtk:\n",
    "    wtk_ij = WTK_idx(wtk, lat_lon)\n",
    "    wtk_lat_lon = wtk['coordinates'][wtk_ij[0], wtk_ij[1]]\n",
    "    print('WTK nearest site coordinates = ', wtk_lat_lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:57:02.098860Z",
     "start_time": "2019-02-07T02:51:03.047777Z"
    }
   },
   "outputs": [],
   "source": [
    "# Extract hourly irradiance, temperature, and wind speed from WTK\n",
    "with h5pyd.File(\"/nrel/wtk-us.h5\", 'r') as wtk:\n",
    "    dt = pd.to_datetime(wtk['datetime'][:].astype(str))\n",
    "    wtk_data = OrderedDict()\n",
    "    for var in ['DIF', 'DNI', 'GHI', 'temperature_2m', 'windspeed_10m']:\n",
    "        wtk_data[var] = wtk[var][:, wtk_ij[0], wtk_ij[1]]\n",
    "\n",
    "    wtk_data = pd.DataFrame(wtk_data, index=dt)\n",
    "    wtk_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine NSRDB and WTK datasets to allow for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:57:02.167779Z",
     "start_time": "2019-02-07T02:57:02.106452Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GHI</th>\n",
       "      <th>DNI</th>\n",
       "      <th>DHI</th>\n",
       "      <th>Wind Speed</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>solar_zenith_angle</th>\n",
       "      <th>dataset</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>96.27</td>\n",
       "      <td>NSRDB</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 01:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>108.13</td>\n",
       "      <td>NSRDB</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 02:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.42</td>\n",
       "      <td>NSRDB</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 03:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>132.95</td>\n",
       "      <td>NSRDB</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 04:00:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>145.54</td>\n",
       "      <td>NSRDB</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     GHI  DNI  DHI  Wind Speed  Temperature  \\\n",
       "2007-01-01 00:00:00  0.0  0.0  0.0         1.9          4.0   \n",
       "2007-01-01 01:00:00  0.0  0.0  0.0         1.8          3.0   \n",
       "2007-01-01 02:00:00  0.0  0.0  0.0         1.9          2.0   \n",
       "2007-01-01 03:00:00  0.0  0.0  0.0         2.2          2.0   \n",
       "2007-01-01 04:00:00  0.0  0.0  0.0         2.6          2.0   \n",
       "\n",
       "                     solar_zenith_angle dataset  year  month  hour  \n",
       "2007-01-01 00:00:00               96.27   NSRDB  2007      1    18  \n",
       "2007-01-01 01:00:00              108.13   NSRDB  2007      1    19  \n",
       "2007-01-01 02:00:00              120.42   NSRDB  2007      1    20  \n",
       "2007-01-01 03:00:00              132.95   NSRDB  2007      1    21  \n",
       "2007-01-01 04:00:00              145.54   NSRDB  2007      1    22  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nsrdb_df = nsrdb_data.copy()\n",
    "nsrdb_df = nsrdb_df.rename(columns={'dhi': 'DHI', 'dni': 'DNI', 'ghi': 'GHI',\n",
    "                                        'air_temperature': 'Temperature', 'wind_speed': 'Wind Speed'})\n",
    "nsrdb_df['dataset'] = 'NSRDB'\n",
    "\n",
    "wtk_df = wtk_data.copy()\n",
    "wtk_df = wtk_df.rename(columns={'DIF': 'DHI', 'temperature_2m': 'Temperature', 'windspeed_10m': 'Wind Speed'})\n",
    "wtk_df['dataset'] = 'WTK'\n",
    "wtk_df['Temperature'] = wtk_df['Temperature'] - 273.15 # Temperature from C to K\n",
    "\n",
    "comp_ds = pd.concat([nsrdb_df, wtk_df])\n",
    "# Extract year, month and hour\n",
    "comp_ds['year'] = comp_ds.index.year\n",
    "comp_ds['month'] = comp_ds.index.month\n",
    "# Shift hour to local time\n",
    "comp_ds['hour'] = (comp_ds.index + pd.to_timedelta('{:}h'.format(site_meta['timezone'][0]))).hour\n",
    "\n",
    "comp_ds.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Irradiance](#Irradiance-Variables) | [Meteorological](#Meterological-Variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Irradiance Variables\n",
    "For irradiance variables non-daylight hours need to be removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:57:02.307308Z",
     "start_time": "2019-02-07T02:57:02.169741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DNI</th>\n",
       "      <th>GHI</th>\n",
       "      <th>DHI</th>\n",
       "      <th>dataset</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01 14:00:00</th>\n",
       "      <td>470.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NSRDB</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 14:00:00</th>\n",
       "      <td>49.096680</td>\n",
       "      <td>19.203796</td>\n",
       "      <td>15.747559</td>\n",
       "      <td>WTK</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 15:00:00</th>\n",
       "      <td>794.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>NSRDB</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 15:00:00</th>\n",
       "      <td>484.832306</td>\n",
       "      <td>205.748169</td>\n",
       "      <td>85.741760</td>\n",
       "      <td>WTK</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 16:00:00</th>\n",
       "      <td>919.000000</td>\n",
       "      <td>426.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>NSRDB</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            DNI         GHI        DHI dataset  year  month  \\\n",
       "datetime                                                                      \n",
       "2007-01-01 14:00:00  470.000000   63.000000  25.000000   NSRDB  2007      1   \n",
       "2007-01-01 14:00:00   49.096680   19.203796  15.747559     WTK  2007      1   \n",
       "2007-01-01 15:00:00  794.000000  252.000000  48.000000   NSRDB  2007      1   \n",
       "2007-01-01 15:00:00  484.832306  205.748169  85.741760     WTK  2007      1   \n",
       "2007-01-01 16:00:00  919.000000  426.000000  58.000000   NSRDB  2007      1   \n",
       "\n",
       "                     hour  \n",
       "datetime                   \n",
       "2007-01-01 14:00:00     8  \n",
       "2007-01-01 14:00:00     8  \n",
       "2007-01-01 15:00:00     9  \n",
       "2007-01-01 15:00:00     9  \n",
       "2007-01-01 16:00:00    10  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Daylight hours have a zenith angle <= 89 degrees\n",
    "daylight = nsrdb_df['solar_zenith_angle'] <= 89\n",
    "daylight = nsrdb_df.loc[daylight].index\n",
    "cols = ['DNI', 'GHI', 'DHI', 'dataset', 'year', 'month', 'hour']\n",
    "solar_comp = comp_ds.loc[daylight, cols].copy()\n",
    "solar_comp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[GHI](#Global-Horizontal-Irradiance) | [DHI](#Diffuse-Horizontal-Irradiance) | [DNI](#Direct-Normal-Irradiance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Global Horizontal Irradiance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:57:04.955888Z",
     "start_time": "2019-02-07T02:57:02.309592Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5965/1477679968.py:7: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(nsrdb, bins=100, ax=ax, kde=False, norm_hist=True)\n",
      "/tmp/ipykernel_5965/1477679968.py:8: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(wtk, bins=100, ax=ax, kde=False, norm_hist=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value = 0.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         2007  2008  2009  2010  2011  2012  2013\n",
       "p-value   0.0   0.0   0.0   0.0   0.0   0.0   0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1    2    3    4    5    6    7    8    9    10   11   12\n",
       "p-value  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          6    7    8    9    10   11   12   13   14   15   16   17   18   19\n",
       "p-value  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "variable = 'GHI'\n",
    "label = 'Global Horizontal Irradiance (W m-2)'\n",
    "\n",
    "nsrdb = solar_comp.loc[solar_comp['dataset'] == 'NSRDB', variable]\n",
    "wtk = solar_comp.loc[solar_comp['dataset'] == 'WTK', variable]\n",
    "fig, ax = plt.subplots(figsize=((6,5)), dpi=100)\n",
    "sns.distplot(nsrdb, bins=100, ax=ax, kde=False, norm_hist=True)\n",
    "sns.distplot(wtk, bins=100, ax=ax, kde=False, norm_hist=True)\n",
    "sns.despine(offset=10, trim=False)\n",
    "plt.ylabel('Probability')\n",
    "plt.xlabel(label)\n",
    "plt.legend(['NSRDB', 'WTK'])\n",
    "plt.show()\n",
    "\n",
    "p_val = wilcoxon(nsrdb, wtk)[1]\n",
    "print('p-value = {:.2f}'.format(p_val))\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Annual Statistics', fontsize=16)\n",
    "sns.boxplot(x='year', y=variable, hue='dataset', data=solar_comp,\n",
    "            showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Year', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = solar_comp.groupby('year')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Monthly Statistics', fontsize=16)\n",
    "sns.boxplot(x='month', y=variable, hue='dataset', data=solar_comp,\n",
    "            showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Month', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = solar_comp.groupby('month')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Diurnal Statistics', fontsize=16)\n",
    "sns.boxplot(x='hour', y=variable, hue='dataset', data=solar_comp,\n",
    "           showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Hour', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = solar_comp.groupby('hour')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take aways:\n",
    "- The WTK reports great GHI than the NSRDB.\n",
    "- The variability of GHI is also larger in the WTK than the NSRDB.\n",
    "- The diurnal pattern of GHI is consistent between the NSRDB and WTK."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Diffuse Horizontal Irradiance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:57:07.681631Z",
     "start_time": "2019-02-07T02:57:04.957575Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5965/129168790.py:7: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(nsrdb, bins=100, ax=ax, kde=False, norm_hist=True)\n",
      "/tmp/ipykernel_5965/129168790.py:8: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(wtk, bins=100, ax=ax, kde=False, norm_hist=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value = 0.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         2007  2008  2009  2010  2011  2012  2013\n",
       "p-value   0.0   0.0  0.17   0.1   0.0   0.0   0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1     2    3    4    5     6    7    8    9    10    11    12\n",
       "p-value  0.01  0.01  0.0  0.0  0.0  0.06  0.0  0.0  0.0  0.0  0.72  0.91"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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r1uHs2bM4evQoTExM4OzsjDFjxqBfv36ytvPmzcOZM2dw+fJlxMXFoaioCI6OjujatStGjBih0b9TIqKnEUi1Oe2biIiIiKgO4phkIiIiIqIqGJKJiIiIiKpgSCYiIiIiqoIhmYiIiIioCoZkIiIiIqIqGJKJiIiIiKrgOskqkkqlKCwshIWFBQQCgb7LISIiojqgoqJC6Z0rSXOMjY0hFNauj5ghWUWFhYXo2LEjzp8/D0tLS32XQ0RERAZMKpUiLS1N4db2pDtCoRDe3t4wNjZ+ZluGZCIiIiItqwzITk5OMDc357fQeiCRSJCSkoLU1FQ0bdr0mX8HDMlEREREWlRRUSELyPb29voup0FzdHRESkoKysvLIRKJntqWE/eIiIiItKhyDLK5ubmeK6HKYRYVFRXPbMuQTERERKQDHGKhf8r8HTAkExERERFVwTHJRERERHqSnp6O3NxcnV3P2toazs7OOrteXcaQTERERKQH6enpCB8bgTJxqc6uKTI2QdSGyFoH5dmzZ2Pnzp2YMWMGJk6cKDt+6NAhTJkyBdeuXQMAbNu2DVFRUbh//z6MjIzg4eGBfv36YdKkSQCAZcuWYfny5QAeL8Pm5OSEHj16YMaMGbCxsZE9bmhoKJKTkwEAJiYmcHBwgL+/P0aOHIkuXbrI2j148ABhYWH/PS+RCK6urhg0aBDeeustjQxtYUgmIiIi0oPc3FyUiUtR3CwEElNrrV9PWJIL3D6O3NxcpXqTTUxM8Msvv2DEiBGwtq5e544dO7Bo0SLMnTsXQUFBEIvFuHbtGm7cuCHXrkWLFli3bh0kEglu3bqFOXPmID8/H0uXLpVr984772D48OEoKytDcnIydu/ejfHjx+Pdd9/FW2+9Jdf2t99+w3PPPQexWIzz58/j448/hqOjI4YNG1b7H0wNVA7JxcXFuHDhAuLj45GWloZHjx7B1NQUdnZ28PHxQVBQEDw9PdUukIiIiKg+k5haQ2LhoO8yatS1a1ckJSVh9erV+PDDD6udP3LkCPr16ycXTFu0aFGtnZGRERwdHQEAzs7O6Nu3L6Kjo6u1s7CwkLVzc3NDYGAgHB0d8eOPP6JPnz5o1qyZrK2NjY2srbu7O6Kjo3HlyhX1nvD/UzokX7hwAVu2bMGBAwdQWloKqVSqsJ1AIEDz5s0xcuRIDBw4kLvSEREREdVBQqEQ77//PmbMmIGIiAi4uLjInXdwcEBcXBySk5Ph7u5eq8d88OABTp069cy1iitFRETgp59+wuHDh+VC8pMSEhLw77//YuDAgbV6zGepdUi+ceMGvvnmG5w6dQpGRkYICgpCu3bt4OfnBwcHB1hbW6OkpAS5ubm4c+cO/vnnH8TExOCLL77A8uXL8fbbb2P06NFo1IgjPIiIiIjqkhdffBGtWrXCjz/+iEWLFsmdmzp1KqZNm4bQ0FB4eXmhffv26NGjB/r27Quh8L+F1K5fv4727dujoqICpaWPx2F/9NFHtbq+jY0N7O3tZeOVK40cORJCoRBlZWUoKyvDiBEjdB+SX331Vbi5uWHu3Lno378/7OzsamwbFBSEESNGAABiY2Oxbds2fP311ygsLKw2loSIiIiIDN/MmTPx2muvYcKECXLHnZycsHXrVly/fh1xcXG4cOECZs+ejR07duDXX3+VBWVvb2+sXLkSpaWl2L17NxITExEeHl7r60ul0moT8pYsWYLmzZujvLwc169fxxdffIHGjRtj5syZaj/fWq+T/Omnn2L//v0IDw9/akCuKigoCIsXL8Zff/2F9u3bq1QkEREREelXYGAgunXrhu+++07heR8fH4wZMwaLFy/G2rVr8ffffyM2NlZ2XiQSwdPTEz4+Ppg5cyaMjIxkK148y6NHj/Dw4UN4eHjIHXd1dYWnpyeaN2+Ofv364bXXXsO6detkPdXqqHVIHjZsmFpDJby8vNC5c2eV709ERERE+jVjxgwcPXoUFy5ceGq75557DsDjhR5q8tZbb2Ht2rVIT09/5nUjIyMhFArxwgsvPLWdUChEeXm5bCtwdXCAMBERERHViq+vL15++WVs2LBBdmz+/PlwcnJC586d4eLigszMTKxcuRJ2dnZo165djY/Vvn17+Pr6YvXq1Zg3b57seGFhITIzM1FeXo4HDx5g9+7d2L59O95///1qK6fl5OQgMzMTFRUVuHbtGiIjIxEcHKyRBSMYkomoXklJSUFBQYHCc5aWlnBzc9NxRURETycs0c2Oe5q6zjvvvIO9e/fKbnft2hW///47Nm/ejJycHNja2qJ9+/b47bffYGtr+9THGjduHGbPno0333wTrq6uAIAff/wRP/74I0QiERwdHdG2bVv89ttvCkckjBs3DsB/y8uFhIRg+vTpGnmeAmlNa7g9xYEDBxAXFwcjIyN069YN3bt3V9hu586d2LlzJyIjI9Uu1NAUFBSgY8eOOH/+PJe3IzIQOTk5GDx4MCQSicLzQqEQ0dHRcrs7ERFpW0lJCe7cuQNvb2+YmprKjteFHffqm5r+LhRRqidZIpFgypQpOHbsmGx95MjISHTp0gXffPMNHBzkF8JOTk5GXFyckuUTEanGxsYGUVFRKCgoQFJSEhYuXIi5c+fKvp6ztLRkQCYig+Hs7IyoDZHIzdVNTzIAWFtbN9iArCylQvLWrVtx9OhR+Pn5Yfz48WjUqBF27NiBkydPYuTIkVi/fn2tF5EmItKGqsMpKmdSExEZImdnZ4ZWA6VUSN65c+fjTz1RUbIu6j59+mDr1q34/PPPMXbsWKxfvx5NmjTRSrFERERERLqgVEi+efMmXnnllWpjOEaMGAEnJye8++67iIiIQGRkJIMyETV4nERIRFR3KRWSpVIprKysFJ7r1asXli1bhmnTpiE8PBzr16/XSIFERHVRTk4OwsPDOYmQiKiOUioku7q64s6dOzWeDwkJwY8//oh33nkHY8eORVBQkNoFEhHVRZxESERUtykVkv39/XHo0CGUlJTUuGxGz549ZUH5yTX0iIgaGk4iJCKqu2q9LTUAhIaGorCwEDt37nxqu549e2LZsmVqbWNNRERERKQvSqXYsLAw7N27FxYWFs9sGxISgj/++ANZWVkqF0dEREREpA9KheRGjRqhWbNmtW7frFkzpdoTERERNSTp6encTMRAaWw8xNWrV3H16lUMHDhQUw9JREREVG+lp6cjYmw4SsVlOrumibEIkRuiahWUN2/ejG+++QZxcXGyIbSFhYUICgpChw4dsGHDBlnbmJgYREREPPMxIyMjkZycjEWLFuHcuXOy47du3cLrr7+Otm3bYvHixTA2Nlbh2WmWxkLyoUOHsGLFCoZkIiIiolrIzc1FqbgMk1vnw82iQuvXSyk0wqorVsjNza1VSA4ODkZRUREuX76Mdu3aAQDOnTsHBwcHXLx4EaWlpTAxMQHwOCQ7OjrKzVtbuHAhCgoK8OWXX8qOWVtbIzk5We46ly5dwptvvokXX3wRn332GYRCpabMaQ1n1hERERHpkZtFBbystB+SldWsWTM4OjoiNjZWFpJjY2MRFhaGs2fP4p9//kFwcLDseLdu3eDo6Ci7v6mpKcRisdyxqs6cOYO3334bo0ePxgcffKDV56Msw4jqRERERGRwgoODERMTI7sdExODoKAgBAYGyo6XlJTg4sWLssBcW//73/8wadIkvPXWWwYXkAGGZCIiIiKqQefOnREfH4/y8nIUFBQgMTFRFpJjY2MBABcuXIBYLFYqJBcVFeHdd9/FhAkTMHHiRG2VrxaNhWR3d3d06tRJUw9HRERERHoWFBSEoqIiJCQk4Pz58/Dy8oKdnR0CAwNl45JjY2PRpEmTahsoPY2JiQm6du2K7du349atW1p8BqrTWEgeNGiQ3CxHIiIiIqrbPD094eLigpiYGJw9exaBgYEAAGdnZ7i6uiI+Ph4xMTHo3LmzUo9rZGSEn376Ca1bt0ZERIRBBmUOtyAiIiKiGgUHByM2NhaxsbEICgqSHe/UqRNOnDiBS5cuKT0eGQCMjY2xfPly+Pv7IyIiAjdv3tRk2WpTa3ULqVSKw4cP4+rVq8jIyEBZWfV1/gQCARYtWqTOZYiIiIhIT4KDg/HZZ5+hvLxcLiQHBQXhs88+Q1lZmUohGXgclH/88Ue8++67iIiIwPr169GiRQtNla4WlUNyUlISJk2ahKSkJEil0hrbMSQTERER1Syl0MigrxMcHIySkhI0a9YMDg4OsuOBgYEoLCyEt7c3nJycVK6rMii/9957sqDs4+Oj8uNpisoh+bPPPsPdu3cxatQoDBgwAE5OTjAy0s1fMhEREVFdZ21tDRNjEVZdsdLZNU2MRbC2tlbqPh4eHrh27Vq14+7u7gqPV/rqq68UHh88eDAGDx4sd0wkEmHFihVK1aVtKofkc+fOITQ0FPPnz9dkPUREREQNgrOzMyI3RCE3N1dn17S2tq7VbnukRki2sLCAp6enJmshIiIialCcnZ0ZWg2UyqtbdO3aFRcuXNBkLUREREREBkHlkPzhhx8iIyMDX3/9NUpLSzVZExERERGRXqk83MLJyQm//vorRo4ciW3btsHT0xOWlpbV2gkEAqxfv16tIomIiIiIdEnlkHzlyhWMHz8eeXl5stuKCAQCVS9BREREVG9IJBJ9l9DgPW3Z4qpUDsmLFi1Cfn4+Zs6ciZdeegmOjo5cAo6IiIioCmNjYwiFQqSkpMDR0RHGxsbsRNQDqVSKzMxMCAQCiESiZ7ZXOST/+++/6NevH9544w1VH4KIiIio3hMKhfD29kZqaipSUlL0XU6DJhAI4OHhUauOXbWWgHty1xUiIiIiUszY2BhNmzZFeXk5Kioq9F1OgyUSiWo98kHlkBwWFoazZ89CIpFAKFR5kQwiIiKiBqHya/7afNVP+qdyuv3ggw9gbGyMmTNnIj09XZM1ERERERHplco9ya+++irKyspw+fJl7Nu3D40bN65xCbhDhw6pVSQRERERkS6pHJKlUikaNWoEV1dXuWOK2hERkf6kpKSgoKBA4TlLS0u4ubnpuCIiIsOnckg+cuSIJusgIiItyMnJQXh4eI3rswqFQkRHR8PGxka3hRERGTiVQzIRERk+GxsbREVFyXqSk5KSsHDhQsydO1e2UyoDMhFRdQzJRET1nKLhFJ6envDx8dFDNUREdQPXbiMiIiIiqoIhmYiIiIioCoZkIiIiIqIqOCaZiNTGJcaIiKi+YUgmIrVwiTEiIqqPlArJv//+Ozp37gx3d3dt1UNEdQyXGCMiovpIqZA8d+5cCAQCuLm5ITg4GMHBwejcuTOcnZ21VR8R1QFcYoyIiOobpULy2LFjERsbi+vXryM6Oho7d+4EADRt2lQWmIOCguDg4KCVYomIiIiIdEHpnmQAyM3NRVxcHM6ePYvY2FjcuHEDSUlJ2L59OwCgWbNmsp7moKAg2NraqlzgypUrsXTpUrRo0QJ//vmn3Ln4+Hh8++23uHLlCiwtLdGvXz9Mnz4dFhYWcu3EYjF++OEH/PHHH8jLy4Ovry/ee+89PP/88yrXRURERET1l0oT96ytrfHCCy/ghRdeAPB44k5sbCxiYmIQGxuLmzdv4vbt29i8eTOEQiH+/fdflYpLS0vD6tWrYW5uXu1cYmIixo0bh+bNm2P27NlIS0vD2rVrcffuXfz6669ybWfPno0DBw4gIiICXl5e2LlzJyZOnIj169ejU6dOKtVGRERERPWXRla3sLGxQe/evdG7d2+IxWIcOXIEK1aswI0bN2qc8V4bX3/9Ndq2bQuJRIJHjx7Jnfv+++/RuHFjbNiwAZaWlgAADw8PfPzxxzh16hS6desGALh06RL++usvfPjhh5gwYQIAYODAgXjppZewePFibNmyReX6iIiIiKh+UnszkfLycpw/fx4//fQTXnvtNQQGBmL69Om4ceMGmjVrhuHDh6v0uHFxcThw4ADmzJlT7VxBQQFOnz6NV155RRaQAeDVV1+Fubk59u3bJzu2f/9+GBkZYcSIEbJjJiYmGDp0KC5cuIDU1FSV6iMiIiKi+kvpnmSJRIKEhATExMQgJiYG8fHxKCkpAQA899xzGDp0KAIDAxEYGAh7e3uViqqoqMDnn3+OoUOHwtfXt9r5a9euoby8HH5+fnLHjY2N0apVKyQmJsqOJSYmwsvLSy5MA0BAQIDsvKurq0p1EhEREVH9pFRIfvPNNxEfH4+ioiIIBAL4+Phg6NChCAoKQqdOndSaoPekLVu2ICUlBb/99pvC85mZmQAAJyenauccHR1x/vx5ubaOjo4K2wFARkZGjXVkZGTIrlVVUVFRjfcjIiIiorpNqZB88uRJCIVCvPjii5gyZYrCXl51PXr0CD/++CPefvtt2NnZKWxT2XNtbGxc7ZyJiYnsfGXbmto9+ViKbN26FcuXL1eqfiIiIiKq+5QKyaGhoTh//jwOHjyI//3vf/Dw8JAt8xYUFAQXFxe1C1q6dCmsra0RHh5eYxtTU1MAj5d2q6q0tFR2vrJtTe2efCxFRowYgdDQUIXnioqKnlojEREREdVdSoXkn376CVKpFImJibIxyQcOHMCOHTsgEAjg7u4uC8zBwcFKj/W9e/cutm3bhjlz5sgNgygtLUVZWRkePHgAS0vLpw6VyMzMlBuG4ejoiPT0dIXtAMVDNio5OTnVeL5yC14iIiIiqn+UnrgnEAjQunVrtG7dGuPHj4dEIsGVK1dkG4scOHAA0dHRstAcHByMhQsX1uqx09PTIZFI8MUXX+CLL76odj4sLAwRERF455130KhRI1y+fBn9+/eXnReLxUhMTES/fv1kx1q2bImYmBgUFBTITd67ePEiAKBVq1bK/giIiIiIqJ5Te51koVAIPz8/+Pn54Y033oBEIsH//vc/LF++HDdu3EBycnKtQ3KLFi2wYsWKaseXLl2KwsJCzJ07F02aNIGVlRW6dOmC3bt34+2335aF3z/++ANFRUXo27ev7L59+/bF2rVrsXXrVtk6yWKxGNHR0Wjbti1XtiAiIiKiajSymUhGRoZst72YmBjcv39fds7IyKjWj2NnZyfbxe9J69evBwC5c9OnT8fIkSMxduxYDB8+HGlpaVi3bh26deuGHj16yNq1bdsWffv2xffff4/s7Gx4enpi586dSoV3IiIiImpYVArJWVlZiI2NlQ2xSEpKAgBIpVIIBAK0bNkSwcHBCA4ORmBgoEYLrtSmTRusW7cOixcvxpdffgkLCwsMHToU77//frW233zzDZYuXYrdu3cjNzcXvr6+WLVqldZqIyIiIqK6TamQvGDBAsTGxuLOnTsA/gvFLVq0QFBQEDp37ozAwEBYW1trtMgNGzYoPN6pU6dabSttYmKCWbNmYdasWRqti4iIiIjqJ6VCcmUgbd68uaynOCgoSGObiBARERERGQKlQvL333+PoKAgODg4aKseIiIiIiK9UyokP7ncGhERERFRfSWsbcOUlBS1L6ZoUw8iIiIiIkNT65Dcu3dvfPrpp3LLu9VGWVkZ/vzzTwwYMADbt29XukAiIiIiIl2r9XCLmTNnYuXKldiyZQs6duyIPn36oG3btmjVqhVEIpFc27S0NFy8eBF///03Dhw4gLy8PHTr1g0vv/yyxp8AEREREZGm1Tokjxs3DoMHD8a6deuwY8cOLFy4EAKBAEKhEFZWVmjcuDFKS0uRm5uL0tJSAI+3sO7WrRvGjx+PLl26aO1JEBERERFpklIT9xo3box3330XU6dOxYkTJ3DmzBlcuHABaWlpSE1NhampKZydneHj44PAwECEhYXB3d1dW7UTEREREWmFSjvuGRkZoVevXujVq5em6yEiIiIi0juVQjIREZEqUlJSUFBQoPCcpaUl3NzcdFwREZFiDMlERKQTOTk5CA8Ph0QiUXheKBQiOjoaNjY2ui2MiEgBhmQiItIJGxsbREVFyXqSk5KSsHDhQsydOxeenp6wtLRkQCYig8GQTEREOqNoOIWnpyd8fHz0UA0RUc1qvZkIEREREVFDwZBMRERERFQFQzIRERERURUaC8k5OTlITU3V1MMREREREemNWiE5Pz8fX3zxBbp27YouXbogLCxMdu7ixYt48803cfnyZbWLJCIiIiLSJZVDck5ODoYNG4aoqCi4uLigefPmkEqlsvO+vr6Ij4/Hnj17NFIoEREREZGuqBySly9fjrt37+L7779HdHQ0+vbtK3fe1NQUgYGBOHv2rNpFEhERERHpksoh+ciRI+jZsyf69+9fYxsPDw+kpaWpegkiIiIiIr1QOSRnZGTgueeee2obkUiE4uJiVS9BRERERKQXKodkGxubZ65mcefOHTg6Oqp6CSIiIiIivVA5JAcGBuLIkSM1Dqe4efMmTp48ia5du6pcHBERERGRPqgckidPnoyKigqMGjUKu3fvxqNHjwAAt27dwvbt2/Haa6/B2NgYEyZM0FixRERERES60EjVO/r6+mLJkiX48MMPMWvWLACAVCrFSy+9BKlUCgsLCyxduhReXl6aqpWIiIiISCdUDskAEBYWhsOHD2PXrl24ePEicnNzYWlpiYCAAAwePBh2dnaaqpOIiIiISGfUCsnA4wl848aN00ApRERERESGQa1tqYmIiIiI6iOVQ/LatWsRHByM9PR0hefT09PRuXNnREZGqlwcEREREZE+qByS9+/fj5YtW8LZ2VnheWdnZ7Rq1Qp79+5VuTgiIiIiIn1QOSQnJSWhRYsWT23z3HPP4e7du6pegoiIiIhIL1QOySUlJTAzM3tqGxMTExQVFal6CSIiIiIivVB5dQtXV1dcuHDhqW3++ecfuLi4qHoJInqKlJQUFBQUKDxnaWkJNzc3HVdERERUf6gcknv27In169djx44dGDp0aLXz27dvx/nz5xEREaFWgURUXU5ODsLDwyGRSBSeFwqFiI6Oho2NjW4LIyIiqidUDskTJ07En3/+iU8++QS7d+/G888/D2dnZ6Snp+PUqVM4d+4cnJycMGnSJE3WS0R4vD55VFSUrCc5KSkJCxcuxNy5c+Hp6QlLS0sGZCIiIjWoHJLt7OwQGRmJDz74ALGxsYiNjYVAIIBUKgUA+Pv7Y/Hixdx1j0hLFA2n8PT0hI+Pjx6qISIiql/U2nGvWbNm+P3333Hp0iUkJCQgPz8fVlZWCAgIgL+/v6ZqJCIiIiLSKbW3pQaAgIAABAQEaOKhiIiIiIj0jttSExERERFVoVZP8sOHD/H777/LhlpUVFRUayMQCLB+/Xp1LkNEREREpFMqh+SrV6/itddeQ15enmyyniICgUDVSxARERER6YXKIfnrr79Gbm4u3nrrLQwdOhQuLi4wMjLSZG1ERERERHqhckj+559/8MILL+Ddd9/VZD1ERERERHqn8sQ9kUiEJk2aaLIWIiIiIiKDoHJIDgwMxOXLlzVZCxERERGRQVA5JM+aNQs3btzAmjVrNFkPEREREZHeqTwmeeXKlWjRogUWL16MLVu2oFWrVrCwsKjWTiAQYNGiRWoVSURERESkSyqH5J07d8r+//79+7h//77CdgzJRERERFTXqBySDx8+rMk6iIiIiIgMhsoh2d3dXZN1EBEREREZDJUn7hERERER1Vcq9yRXKi0tRUJCAjIyMiAWixW2GThwoLqXISIiIiLSGbVC8saNG/HDDz8gPz9f4XmpVAqBQMCQTERERER1isrDLQ4ePIjPP/8cLi4umDVrFqRSKcLCwjB9+nR0794dUqkUvXv35soWRERERFTnqByS169fD3t7e2zduhXjxo0DALRs2RITJ07Ezz//jG+//RaHDx+Gm5ubpmolIiIiItIJlUPytWvXEBoaCjMzM9kxiUQi+/+XX34ZnTt3xooVK9SrkIiIiIhIx1QOyeXl5bCzs5PdNjU1RV5enlwbX19fXLlyRfXqiIiIiIj0QOWQ7OTkhIyMDNltNzc3JCYmyrVJSUmBkZGR6tUREREREemByiHZ399frpe4e/fuiI+Px+rVq3Hjxg1s2bIF//vf/+Dv76/U4964cQPvvPMOwsLC0LZtWwQHB2PMmDE4cuRItba3bt3ChAkT0L59ewQFBeGDDz7Aw4cPq7WTSCT45ZdfEBoaCn9/f7z88sv4888/lX/SRERERNQgqByS+/btC7FYjAcPHgAAJk2aBBcXFyxduhSvvPIKFixYAHNzc3zwwQdKPW5KSgoKCwsxaNAgzJ07F2+//TYA4K233sLWrVtl7dLS0jBmzBjcu3cP06dPx+uvv47jx49j/Pjx1dZrXrJkCRYvXoznn38en3zyCdzc3DBjxgz89ddfqj59IiIiIqrHVF4n+cUXX8SLL74ou21nZ4ddu3Zh+/btuH//Ptzd3fHqq6/C2dlZqccNCQlBSEiI3LHw8HAMHjwY69atw4gRIwAAq1atQnFxMaKjo2UraAQEBGD8+PHYuXOnrF16ejrWrVuHMWPGYN68eQCAYcOGITw8HN988w369u3LISFERA1QSkoKCgoKFJ6ztLTk6kxEDZzaO+49ydraGm+88YYmHxIAYGRkBFdXVyQkJMiOHTx4ED179pR7EevatSu8vLywb98+WUg+dOgQysrKMHr0aFk7gUCAUaNGYcaMGbhw4QI6deqk8ZqJiMhw5eTkIDw8XG5VpicJhUJER0fDxsZGt4URkcHQaEjWpKKiIpSUlKCgoABHjhzBiRMn0K9fPwCPe4ezs7Ph5+dX7X4BAQE4ceKE7HZiYiLMzc3RvHnzau0qzzMkExE1LDY2NoiKikJBQQGSkpKwcOFCzJ07F56engAe9yQzIBM1bLUOybt27QIAvPDCC7C0tJTdrg1VtqX+6quvZGOQhUIhXnzxRdlwicpVNRwdHavdz9HRETk5ORCLxTA2NkZmZibs7e0hEAiqtXvysRTJyMhAZmamwnNFRUVKPyciIjIcVYdTeHp6wsfHR0/VEJGhqXVInj17NgQCAdq2bQtLS0vZ7aeRSqUQCAQqheTXXnsNffv2RUZGBvbt2weJRIKysjIAQGlpKQDA2Ni42v1MTEwAACUlJTA2Npb9+bR2Ndm6dSuWL1+udO1EREREVLfVOiQvWrQIAoFA1gP75Zdfaq0oAGjevLlsiMTAgQPx+uuvY/Lkydi+fbss4FZdxQL4L0CbmprK/qxNO0VGjBiB0NBQheeKiooQHh6uxDMiIiIiorqi1iF58ODBcrcHDRqk8WKepk+fPpg3bx7u3LkDJycnAFA4FCIzMxM2Njay3mNHR0fExMTIerWfbAdA9liKODk51Xi+phnRRERERFT3qbxOsq5VDosoKCiAs7Mz7OzscPny5WrtLl26hJYtW8put2rVCsXFxbh165Zcu4sXL8rOExERERE9yeBCcnZ2drVjZWVl+OOPP2BqaiobgtG7d28cO3YMqampsnZnzpzB3bt30bdvX9mxsLAwiEQibNq0SXZMKpViy5YtcHZ2Rvv27bX4bIiIiIioLqr1cIuWLVs+c6KeIgKBQG776meZN28eCgoKEBgYCGdnZ2RmZmLPnj24ffs2Zs+eDQsLCwDA5MmTsX//fkRERCAiIgJFRUVYs2YNfHx8MGTIENnjubi4ICIiAmvWrEF5eTn8/f1x6NAhnDt3DosXL+ZGIkRERERUTa1DcmBgYLVjeXl5uHbtGoyMjODi4gIHBwdkZWUhLS0NFRUV8PX1RePGjZUqqH///tixYwc2b96MnJwcWFhYoE2bNpg5cybCwsJk7VxdXREVFYWvvvoK3333HUQiEUJCQjB79uxqq1nMnDkT1tbW2Lp1K6Kjo+Hl5YVvv/0WL7/8slK1EREREVHDUOuQvGHDBrnbaWlpGDVqFF566SW8//77cutNpqSk4LvvvsOFCxewevVqpQoaMGAABgwYUKu2LVq0wJo1a57ZTigUYtKkSZg0aZJStRARERFRw6TymOSvv/4ajo6OWLx4cbUF2d3c3PDdd9/BwcEB33zzjdpFEhERERHpksrbUp8+fRojR458apvOnTtj27Ztql6CyCClpKQoXALQ0tKy2gdGIiIiqptUDslisfipWzoDj7d1rty0g6g+yMnJQXh4OCQSSbVzQqEQ0dHRsLGx0X1hREREpFEqh+Q2bdpg7969GD58uMJl1OLj47F37160bdtWrQKJDImNjQ2ioqJQUFCApKQkLFy4EHPnzoWnpycsLS0ZkImIiOoJlUPye++9h3HjxmHMmDHo1asXOnbsCDs7Ozx8+BDnzp3DsWPHYGRkhPfee0+D5RLpX9UhFZ6envDx8dFTNURERKQNKofkTp064ZdffsEnn3yCw4cP4/DhwxAIBJBKpQAADw8PfP755+jYsaPGiiUiIiIi0gWVQzIAdOnSBf/73/9w/vx5XL16Ffn5+bCyskLLli3RsWNHlTYfISIiIiLSN7VCMvB4R71OnTqhU6dOmqiHiIiIiEjvVF4nmYiIiIiovlK7J/nChQs4ffo0MjIyIBaLq50XCARYtGiRupchIiIiItIZlUNyeXk53n//ffzvf/+DVCqVm7QHQHabIZmIiIiI6hqVQ/LatWtx8OBBDBkyBKNHj8aQIUPw2muvoX///oiLi8Mvv/yCLl26YObMmZqsl4hIK2raSRHgbopERA2RyiF5z549aNGiBRYuXCg7ZmVlhbZt26Jt27YICQnBsGHD0Llz52duX01EpE9P20kR4G6KREQNkcoh+d69exg2bJjstkAgQHl5uex2ixYt0KtXL2zevJkhmYgM2tN2UgSg090U2aNNRGQYVA7JIpEIpqamstvm5uZ4+PChXBs3NzccPXpU9eqIqF4zpEBoCDspskebiMhwqBySXV1dkZqaKrvdrFkzxMXFySbrAcDFixdhbW2tfpVEVO8wEFb3ZI82gGq92rrs0QYM60MMEZGuqRySAwMDcfjwYVko7t+/P77++mtMmjQJISEhOH/+PM6fP48hQ4Zosl4iqicMaYiDIVEUPNmrTUSkeyqH5CFDhqCiogLp6elwcXFBeHg4YmJicOzYMZw4cQIAEBAQgBkzZmisWCKqXwxhiAMpZmi92kREuqZySG7Tpg0+/fRT2W2RSIRVq1YhISEB9+/fh5ubGwICAiAUclM/IqK6yFB6tYmI9EHlkLxr1y7Y29uje/fucsf9/f3h7++vdmFERERERPqicjfv3LlzcfLkSU3WQkRERERkEFQOyY6OjqioqNBkLUREREREBkHl4RahoaH4+++/IRaLYWxsrMmaiIhqLT09Hbm5udWOJyUlyf1ZlbW1NZydnbVaGxER1V0qh+Tp06fjn3/+wdSpU/HBBx+gRYsWmqyLiLSkPq19m56ejvCxESgTl9bYZuHChQqPi4xNELUhkkGZiIgUUjkkDxw4EGKxGImJiTh58iRMTExgZ2cn20ikkkAgwKFDh9QulIjUV9/Wvs3NzUWZuBTFzUIgMa39xkXCklzg9nHk5uZqNCSzV1t76tOHOyKqG1QOyVKpFCKRCK6urtWOP+02EelPfV37VmJqDYmFg15rYK+29tS3D3dEVDeoHJKPHDmiyTqISEe49q12GFqvdn1SXz/cEZFhUzkkf/TRR/D19cW4ceM0WA4RUd1mCL3a9RE/3BGRrqm8BNyff/6J7OxsTdZCRERERGQQVA7JTZs2RWZmpiZrISIiIiIyCCqH5CFDhuDYsWNIT0/XZD1ERERERHqn8pjk3r17IyYmBiNHjsQbb7wBf39/2NvbV1sCDlA8loyIiIiIyFCpHJJfeOEFCAQCSKVSfPHFFzW2EwgEuHLliqqXISIiIiLSObU2E1HUa0xEREREVNepHJK/+uorTdZBRERERGQwVA7JREREpF/crptIexiSiYiI6iBu102kXUqF5DfffFPpCwgEAvz8889K34+IiIhq9uR23VW36gbA7bqJ1KRUSD558qTSF+DkPiIiIu2oOpyCW3UTaY5SIfnw4cPaqoOIiDQoPT0dubm51Y4nJSXJ/VmVtbU1nJ2dtVobEVFdoFRIdnd311YdRESkIenp6QgfG4EycWmNbRYuXKjwuMjYBFEbIhmUiajB48Q9IlKJKj2V7KXUjdzcXJSJS1HcLAQSU+ta309YkgvcPo7c3Fz+PRFRg8eQTHWCqssccXkk7VC1p5K9lLolMbWGxMJB32UQEdVJDMlk8FRd5siQlkeqb2FdlZ5K9lJSfVHffp+JSDGGZDJ4qi5zZCjLIxlSWNc09lRSQ1Off5+JSB5DMtUJqi5zZAjLIz0Z1gFUC+xcy5So7jCUD99EpH0MyUQ6oOjrV65nSlQ3GcKHbyLSPqG+CyAiIiIiMjQMyUREREREVdR6uEVYWJhKFxAIBDh06JBK9yUiImoIuGIGkeGpdUiWSqUqXUDV+xERETUEXDGDyDDVOiQfOXJEm3UQERE1SFwxg8gwcXULIiIiPeOKGUSGhxP3iIiIiIiqULsnOS0tDWfPnkVGRgbEYnG18wKBAFOmTFH3MkREREREOqNWSP7666+xYcMGVFRUyI5JpVIIBAK5/2dIJiIiIqK6ROXhFtu2bcO6desQHByMH3/8EVKpFAMHDsT333+PkSNHwsjICH379sX69es1WS8RERERkdap3JO8detWuLu745dffoFQ+Dhru7u7o3///ujfvz/69euH119/HX379tVYsUREVPekp6cjNze32vGkpCS5P6uytraGs7OzVmsjIqqJyiH59u3bePXVV2UBGYDcsIugoCCEhIRg7dq1SgXlS5cuYdeuXYiJiUFycjJsbGzQtm1bvPfee/D29pZre+vWLSxatAjx8fEQiUQICQnBRx99BDs7O7l2EokEa9aswebNm5GZmQkvLy9MmjQJL730korPnoiIaiM9PR3hYyNQJi6tsc3ChQsVHhcZmyBqQySDMhHphVpjkhs3biz7fzMzM+Tk5Mid9/b2xunTp5V6zF9//RXx8fHo27cvfH19kZmZiY0bN2Lw4MHYunWrbEmctLQ0jBkzBlZWVpg+fTqKioqwdu1aXL9+Hdu3b4exsbHsMZcsWYKff/4Zw4cPh7+/Pw4fPowZM2ZAIBBgwIABqv8AiMgglTxKQ3bCCYjzsmDc2AH2/j1gauui77IapNzcXJSJS1HcLAQSU+ta309YkgvcPo7c3FyGZCLSC5VDsrOzM9LS0mS3mzZtiosXL8q1uXHjBszNzZV63HHjxmHx4sVyIbd///54+eWX8fPPP2Px4sUAgFWrVqG4uBjR0dGy9SUDAgIwfvx47Ny5EyNGjADwuBdj3bp1GDNmDObNmwcAGDZsGMLDw/HNN9+gb9++MDIyUv4HQEQGKTvhBJIOroORiRnMHJsi7+5lpMftg2ef12Hv113f5TVYElNrSCwc9F0GEVGtqTxxr0OHDnKhOCwsDImJiZg3bx6OHTuG7777DidOnECnTp2UftwnAzIAeHl5oUWLFrh9+7bs2MGDB9GzZ0+5Bdi7du0KLy8v7Nu3T3bs0KFDKCsrw+jRo2XHBAIBRo0ahbS0NFy4cEGp+ojIcJU8SkPSwXWw9+8O/0lL4TNiNvwnLYW9X3ckHViLkkfpeqkp+cQ23PnzJySf2IaSR2nPvhMREemdyiH51VdfRZMmTZCcnAwAmDBhAlq1aoVt27bhrbfewi+//AI3Nzd8+OGHahcplUqRlZUFW1tbAI97h7Ozs+Hn51etbUBAABITE2W3ExMTYW5ujubNm1drV3meiOqH7IQTMDIxQ5PQcAhFjz9sC0XGaBIaDiMTM2QnHNd5PVfWfoSsS8dQVpiHrEvHcGXtR8i+fFKndRARkfJUHm4RHByM4OBg2W0LCwts3boVhw8fxr179+Du7o5evXopPdxCkd27dyM9PR3vvPMOACAjIwMA4OjoWK2to6MjcnJyIBaLYWxsjMzMTNjb28vWbn6y3ZOPpUhGRgYyMzMVnisqKlLpuRCR9ojzsmDm2BTCRvLfRglFxjBzbApxXpbOanmyV7tJr8ehXVImxv0jUUg6sBYW7j4wteVYWyIiQ6X2jntPEolEGl/y7datW/jss8/Qvn17DBo0CABQWvp4lnTVYRkAYGJiAgAoKSmBsbGx7M+ntavJ1q1bsXz5crWfAxHphnFjB+TdvQxJmVjWkwwAkjIxijPvwcK1p85qkevVbiTfq51z4xyyE47DvcdwndVDRETKUTkkh4WF4bXXXkNERESNbTZu3Ii1a9fi8OHDKl0jMzMTkyZNgpWVFX744QfZBLvKgKtoG+zKAG1qair7szbtFBkxYgRCQ0MVnisqKkJ4eLgSz4aItM3evwfS4/bh/pEo2ZCLyt7bitJi2PuH6KwWQ+rVJiIi5akckpOTk5GXl/fUNnl5eUhJSVHp8fPz8/Hmm28iPz8fGzdulFsCyMnJCQAUDoXIzMyEjY2NrPfY0dERMTExcttlP3nfysdSxMnJqcbzBQUFyj8pItIqU1sXePZ5HUkH1iLnxjmYOTZFceY9VJQWw7PP6zod3mBIvdpERKQ8jQ63qCo/P1/hUIdnKS0txeTJk3H37l2sW7cOzz33nNx5Z2dn2NnZ4fLly9Xue+nSJbRs2VJ2u1WrVti+fTtu3bol9ziVK3O0atVK6fqI9IU7lz2bvV93WLj7IDvhOMR5WbBw7Ql7/xCdj/81pF5tIiJSnlIhOS4uTu52cnJytWPA45330tLSsGfPHnh5eSlVUEVFBd577z38888/+Omnn9C+fXuF7Xr37o1du3YhNTUVrq6uAIAzZ87g7t27GDdunKxdWFgYvvzyS2zatEm2TrJUKsWWLVvg7Oxc4+MTGRruXFZ7prbOeh/va0i92kREpDylQvLYsWNlQxYEAgF27dqFXbt2KWxbObxhxowZShX01Vdf4ciRI+jVqxdycnLwxx9/yJ1/9dVXAQCTJ0/G/v37ERERgYiICBQVFWHNmjXw8fHBkCFDZO1dXFwQERGBNWvWoLy8HP7+/jh06BDOnTuHxYsXcyMRqjO4c1ndYyi92kREpDylQvKUKVMgEAgglUqxYsUKBAYGyi0DV0koFMLa2hqdO3eutj7xs1y9ehUAcPToURw9erTa+cqQ7OrqiqioKHz11Vf47rvvIBKJEBISgtmzZ1cb4jFz5kxYW1tj69atiI6OhpeXF7799lu8/PLLStVGZAi4c1ndYgi92pW4XTcRUe0pFZKnTZsm+//Y2FgMGTIEAwcO1GhBGzZsqHXbFi1aYM2aNc9sJxQKMWnSJEyaNEmd0ojoGRjCDBe36yYiUo7KE/eUCbNEVP8xhBkubmxCRKQ8jaxuUVRUhPz8fFRUVCg87+bmponLEJGBqk0IMzfm+H994cYmRETKUyskb9++HevWrcOdO3dqbCMQCHDlyhV1LkNEBq42Icy8o+KNeUj7uLEJEZHyVA7JmzZtwmeffYZGjRqhU6dOcHFxQaNGWl12mYgMFEOYYePGJkREylM51a5fvx62trbYtGkTvL29NVkTEdUxDGGGjRubEBEpT+WQnJKSgmHDhjEgExFDmIHjxiZERMpTOSQ7OjrWOFGPiBqWWoWwQsMbcqFoG299bfGt7Vq4sQkRkXJUDsmDBg3Czp07UVRUBHNzc03WRER1UF0KYYKyIgggrXEbb6DmLb5NjEWI3BClsaCsy1oMaWMTIiJDp3JIfuutt3Djxg28/vrrmDFjBlq3bg0LCwtN1kZEdUxdCWGCcjGkEGBy63y4WdT+G7GUQiOsumKl0S2+DakWIiL6j8oh2d/fHwAglUoRERFRYzsuAUdEhsrNogJeVoYxbMyQaiEiIjVCcqdOnTRZBxERGQhDGqtNRKQv3JaaiHSKAcxwGdJYbSIifePuH0SkEwxghk+X46NLHqUhO+EExHlZMG7sAHv/HjC1dVG1dCIijdNISE5PT0diYiIKCgpgaWmJVq1a8c2MiORwglrdoe3x0dkJJ5B0cB2MTMxg5tgUeXcvIz1uHzz7vA57v+5auy4RkTLUCslJSUlYsGABzp49W+1cly5dMH/+fHh6eqpzCSKqZzhBrWEreZSGpIPrYO/fHU16yW88k3RgLSzcfbS2bGB6ejpyc3OrHedwHyJSROWQnJqaitGjRyM7OxvNmjVDp06d4OTkhMzMTJw7dw6nT5/GmDFjsH37dri6umqyZiIiqqOyE07AyMTs8c6MjR5vYS4UGaNJaDhybpxDdsJxrSwjmJ6ejvCxESgTl9bYpqbhPiJjE0RtiGRQJmpgVA7Jy5cvR3Z2NubPn4+RI0dCIBDInd+yZQsWLFiAFStW4IsvvlC7UCJSjGM766c72aXYFv8ID3LK4GEjwvAOtvC2N9F3WWoT52XBzLGpLCBXEoqMYebYFOI87ezMmJubizJxKYqbhUBial3r+wlLcoHbxznch6gBUjkknzp1Cr169cKoUaMUnh85ciSOHz+OEydOqFwcET0dx3ZqlqEE020XHmH27mRYmRihlYspTtwqwOrTWfj6FXcMa2+r83o0ybixA/LuXoakTAyh6L+gLCkTozjzHixce2r1+hJTa0gsHLR6DSKqH1QOydnZ2fDx8XlqGx8fH5w8eVLVSxDRU+hzbGd9ZCjB9E52KWbvTsbw9rZY0M8VpiIhSsokmL83FbN2JyOwqTm86nCPsr1/D6TH7cP9I1GPh1w88e+2orQY9v4h+i6RiAiAGiHZzs4ON2/efGqbmzdvws7OTtVLENFT6GtspyESFuco1V5Qmi9325CC6bb4R7AyMZLVAQCmIiE+7e+KfYl52Br/CLNerLvDaUxtXeDZ53UkHViLnBvnYObYFMWZ91BRWgzPPq9X+2Cn7LranGRHRJqickju1q0bdu7cie3bt2PYsGHVzu/YsQNHjx7FoEGD1CqQSN8MdUa8smM76/MmHmZ3VBvWlVJoBAD45WwuLIyNML67B9JKhEAJABjh9e4e+OtKHn4+m4uJPVxl7bWh8rGvZpXD28EMaSWi/6+jkhGaOZjhalY57uYbabUWbbP36w4Ldx9kJxyHOC8LFq49Ye8fIheQVV1Xm2tqE5GmqBySp06diqNHj2LevHmIjIxEYGAg7O3tkZ2djbi4ONy8eRO2traYOnWqJusl0ilDnhFf27GdDWETj2LvHpCY2dS6vVHOfZimXMCqK1YAgCt3UiA0tcLCC9W/+TIytcLBO1I8UOLxVVFZy+3Ch0hNTcXcs1YwMvovCFdUVCAhtQSurq6YF6fdWnTB1Nb5qd90qLKuNtfUJiJNUjkku7m5YfPmzZg3bx5iY2Nx48YNufPBwcFYsGABl3+jOs2QZ8TXdmxnQ9jEQ2Jmo9RkrMrhGZU/k5+LBdhzKR9z2z2Eyf8PcQCA0jIJhsTkY4CPPSYG5sh+JrV57NqqHPpRWcuD5ywQsbYc1rlX8G6oO0xEQpSWSfDDkWRIK8rxVW8LeNjWrpb6gOtqE5G+qLWZiJeXFyIjI5Gamlptxz2GY6pPDHFGvLJjOxk2qqv8mbzZ2RpbzmVg7ckH+LT/E2OSj6SiSFyBiZ2ta/2zU3XoR2UtXlaN8PWr7pi1Oxl/38xFKxdTXEkrQUFpBb5+1R3dmjYCwL9HIiJt08i21K6urgzFRHpQm7Gd2qRMr2nVyXKGxNveBF+/8jiY7k/Mkw+mr7grNWlP1aEfTxrW3haBTc2x9f+XoxvT0RYjOtjqbVULQ1kaj4hIl1QOyR999BHMzc0xbdo02NjYKGxz6NAhHD58GF9++aWqlyGiZ3jW2E5tUrXX9EmGEsA0FUxVHfpRlZe9iUGsYmEoS+ORYUtJSUFBQYHCc5aWlnBzc9NxRUTqUzkk79y5EwKBAKdOncLq1avh5eVVrc3Vq1exa9cuhmSiekqZXlNFPaaGFsAMJZgaCkNaGo8MV05ODsLDwyGRSBSeFwqFiI6OrrFDjchQqTXcwt/fH4mJiRgxYgR+/PFHBAcHa6ouMgDsGaBnUabXtGqPKQOY4TKkpfHI8NnY2CAqKkr2fpGUlISFCxdi7ty58PT0hKWlJQMy1UlqheQePXrgww8/xJQpUzBhwgQsWLAAQ4cO1VRtpEfsGSBtYQDTHVVX2jCkpfGoblDUaeLp6fnMnXmJDJnaE/c6deqEbdu2YeLEifjkk09w584dfPDBB5qojfToyZ6Bqr0CANgzQCpjANMdVceMa2NpPCKiukYjq1t4enpi+/btmDJlCtauXYt79+7h22+/1cRDkx5V7RlgrwBpAgOY7qi60oY2lsar6+rzjpVEpJhGQjIANG7cGOvWrcMnn3yCnTt3Ijw8HK1atdLUwxPVSXxjrY4BTHfUXWlDk0vj1VUNYcdKIlJMYyEZABo1aoQvv/wSXl5eWLp0Kf79919NPjxRncE31mdjAKsbNLU0nqrjo/WtIexYSUSKqRySAwMD4eHhofDcpEmT4OnpiVmzZkEsFqtcHFFdxTfW2jG0TTNIMU0sjaeJNbX1iTtWEjU8KofkDRs2PPV837590bdvX1Ufnqhe0MYba13tkasJ1yZuGDSxEyERkS5pdLgFEcD1lbWtrvfIUcOkqZ0IDWWHRiKq/2odkuPi4gAAAQEBMDExkd2ujcDAQOUrozqJ6ytrH3vkqKEytB0aiah+q3VIHjt2LAQCAfbu3Qtvb2/Z7dpITExUuUCqW7i+svaxR44aotrs0Ahjc32XSUT1SK1D8pQpUyAQCGBrayt3m6gqrq9s+NgjR3XNtvhHsDIxkgVkADAVCfFpf1fsS8zD1vhHGNFZ+ZBc8igN2QknIM7LgnFjB9j794CpLcfIE5ESIXnatGlPvU1EdUNteuS4ugQZisptya9mlcPbwQxpJaL/38K8khGaOZjhala50luYZyecQNLBdTAyMYOZY1Pk3b2M9Lh98OzzOuz9umvuSRBRnaTyxL3ly5fDw8MDAwcO1GA5RHVDXe59qk2PHFebIENRudvi7cKHSE1NxdyzVjAy+i8MV1RUICG1BK6urkrtzFjyKA1JB9fB3r87mvQKh1BkDEmZGPePRCHpwFpYuPvA1NbwlmFMT09Hbm5uteOa2qSIE6+J/qNySF61ahUiIiI0WQtRnVBXe5+U6ZG7m2+kdK8ckTZUrjX+4DkLRKwth3XuFbwb6g4TkRClZRL8cCQZ0opyfNXbAkLj/FoH5eyEEzAyMUOT0HAIGxkDAIQiYzQJDUfOjXPITjgO9x7DtfnUlJaeno6IseEoFZfV2EadTYo48ZpInsoh2dXVFfn5hr3+KjU82u5lqau9T4ByPXLz4mz0VCWRvMq1xr2sGuHrVx/v0Pj3zVz5HRpfdUe3po1wN7/2a5KL87Jg5thUFpArCUXGMHNsCnFelqafitpyc3NRKi7T2iZFT068BlBt8jUnXlNDo3JI7t+/P/744w/k5+fDyqr2X3ERaYu2e1mAutn7VEmZHjkP2xzZGyuRodDkDo3GjR2Qd/cyJGViCEX/BWVJmRjFmfdg4dpTg5VrljZ3/1M0nIKTr6mhUjkkT5kyBVevXsVrr72Gd955B/7+/rC3t9dkbURK0XYvC1A3e58qKdMjB3D7XTJMmtqh0d6/B9Lj9uH+kajHH3qf+FaoorQY9v4hGqiWiOoylUNy27ZtAQBSqRRvvfVWje0EAgGuXLmi6mWIlKbNXpa63Pv0JE32yBHVRaa2LvDs8zqSDqxFzo1zMHNsiuLMe6goLYZnn9cNdtgUEemOyiG5U6dOmqyDqE6oT71PmuqRI6qr7P26w8LdB9kJxyHOy4KFa0/Y+4cwIBMRADVC8oYNGzRZB1GdwN4novrF1NZZL/MIVJlkXNPEYyLSDpVDMlFDxd4nIlJnW/f09HSEj41Ambi0xjY1TTImIt3RSEhOT09HYmIiCgoKYGlpiVatWtVqOS2imhh6L4u+ep+o/lJ2XWquY60/6m7rnpubizJxKYqbhUBial3r6xrlPoBpcrw6pROREtQKyUlJSViwYAHOnj1b7VyXLl0wf/58eHp6qnMJaoDYy0K6YgjBVNrIGAJIVVpuz8RYBGvr2ocsUp8mt3WXmFpDYuFQ62sLi3NUrJqIVKFySE5NTcXo0aORnZ2NZs2aoVOnTnByckJmZibOnTuH06dPY8yYMdi+fTtcXV01WTPVc4bYy6Lsm5OgVPFGO+p8RUuaY0jBVCoyhxQC2YYNT6q6mUNVtd0EhzSH27r/R9ubNxHpm8ohefny5cjOzsb8+fMxcuRICAQCufNbtmzBggULsGLFCnzxxRdqF0oNjyH1spjdOaH2Y6j7FS1pjiEG06dt2MDNHDRH1Q+83NZdni42byLSN5VD8qlTp9CrVy+MGjVK4fmRI0fi+PHjOHFC/XBBpG/F3j0gMbOpdXujnPswTbkgu63Jr2hJcxhMGx5VP/ByW3d5uti8iUjfVA7J2dnZz3wD8fHxwcmTJ1W9BJHBkJjZqNWrza9otUtYUv0rX0221+Zja7MWqk7VD7zc1l0xbW7eRKRvKodkOzs73Lx586ltbt68CTs7O1UvQVTn8Sta7bK2tobI2AS4fVzp+4qMTTQ6ttiQalGXIUxo1BZVP/ByW3eihkflkNytWzfs3LkT27dvx7Bhw6qd37FjB44ePYpBgwapVSAZPk7eqBm/otUuZ2dnRG2IrPHfny7HFhtSLaoypAmNhozbuhM1DCqH5KlTp+Lo0aOYN28eIiMjERgYCHt7e2RnZyMuLg43b96Era0tpk6dqsl6ycBw8sbT8Sva6jTdS+ns7PzUf0O6HFtsSLWowhAnNBoqbutOVP+pHJLd3NywefNmzJs3D7Gxsbhx44bc+eDgYCxYsIDLv9VznLzxdPyK9j/spaw7OKGRiEjNzUS8vLwQGRmJ1NTUajvuqRqOCwsLsWbNGly8eBEJCQnIzc3Fl19+icGDB1dre+vWLSxatAjx8fEQiUQICQnBRx99VG0ctEQiwZo1a7B582ZkZmbCy8sLkyZNwksvvaRSjVQdJ288W338ilaZSWeCijL2UuoYJxESEalOI9tSu7q6aqzH+NGjR1ixYgXc3Nzg6+uL2NhYhe3S0tIwZswYWFlZYfr06SgqKsLatWtx/fp1bN++HcbGxrK2S5Yswc8//4zhw4fD398fhw8fxowZMyAQCDBgwACN1E1UG/XlK1pVJ6mJjE0QEBBQY+BlL6Vm1KdJhERE+qKRkKxJTk5OOHXqFBwdHZGQkIChQ4cqbLdq1SoUFxcjOjoabm5uAICAgACMHz8eO3fuxIgRIwA8HjO7bt06jBkzBvPmzQMADBs2DOHh4fjmm2/Qt29fuYlURPRsqk5SY4+wbtSHSYRERPqmVEhesGCB0hcQCASYP39+rdsbGxvD0dHxme0OHjyInj17ygIyAHTt2hVeXl7Yt2+fLCQfOnQIZWVlGD16tFxNo0aNwowZM3DhwgV06tRJiWdEREDdn6RW3/Hvh4hIPUqF5C1btig8LhAIIJVKazynTEiujfT0dGRnZ8PPz6/auYCAALld/hITE2Fubo7mzZtXa1d5niGZiIjqivq8jjWRIVEqJEdGRlY7tnPnTuzatUvhOW3JyMgAAIU9zo6OjsjJyYFYLIaxsTEyMzNhb28PgUBQrd2Tj1XTdTIzMxWeKyoqUrV8IiKqJWUCXkMJg/V9mUgiQ6FUSA4KCqp2rHJinaJz2lJaWgoAcpPzKpmYPF4poKSkBMbGxrI/n9auJlu3bsXy5cs1UTIRUYOm7MoZgopSlZYMbAjLBaq65CYRKcfgJu7VRmXAFYvF1c5VBmhTU1PZn7Vpp8iIESMQGhqq8FxRURHCw8OVK5yIqIFRZ6UNI5EJPv/sU9jb28sdb+iTQ6suuXknuxTb/n9pSQ8bEYZ3sIV3HV5akshQ1MmQ7OTkBAAKh0JkZmbCxsZG1nvs6OiImJgYSKVSuSEXlfetfKyarlPT+YKCApXrJ1KH0j1yYv5bJf3R5kobnHwIbLvwCLN3J8PKxAitXExx4lYBVp/OwtevuGNYe1t9l0dUp9XJkOzs7Aw7Oztcvny52rlLly6hZcuWstutWrXC9u3bcevWLTz33HOy4xcvXpSdJ6oL1OmRAzjZh/SHK21ox53sUszenYzh7W2xoJ8rTEVClJRJMH9vKmbtTkZgU/M6vVkRkb7VyZAMAL1798auXbuQmpoq28jkzJkzuHv3LsaNGydrFxYWhi+//BKbNm2SrZMslUqxZcsWODs7o3379voon7SovoZBVXvksrOzsWD+PG4HTaQGQ3xd2Rb/CFYmRrKADACmIiE+7e+KfYl52Br/SOubFxniz4VIUwwyJEdFRSEvL0+28sTRo0eRlpYGABg7diysrKwwefJk7N+/HxEREYiIiEBRURHWrFkDHx8fDBkyRPZYLi4uiIiIwJo1a1BeXg5/f38cOnQI586dw+LFi7mRSB1S8igN2QknIM7LgnFjB9j794CpbfU3gPo8QUXVHrnIDVHcWIJIBdJGxipNIASU/5BZ29e4Sg9yytDKxVQWkCuZioRo7WKKBzllStesrPr8ekukVEh+8803qx27d+9ejeeAx+sk//zzz0oVtXbtWiQnJ8tuHzx4EAcPHgQAvPLKK7CysoKrqyuioqLw1Vdf4bvvvoNIJEJISAhmz55dbTWLmTNnwtraGlu3bkV0dDS8vLzw7bff4uWXX1aqLtKf7IQTSDq4DkYmZjBzbIq8u5eRHrcPnn1eh71fd7m2nPldHb/uJlKNVGQOKQQKP0hq8kOmMq9xlTxsRDhxqwAlZRK5oFxSJsGVtBKM6aj9Mcl8vaX6TKmQfPLkSaXPVV2fuDaOHDlSq3YtWrTAmjVrntlOKBRi0qRJmDRpktK1kP6VPEpD0sF1sPfvjia9wiEUGUNSJsb9I1FIOrAWFu4+MLX9742o6sxvfeJXkUT1w9M+SKr7IVPZ17hKwzvYYvXpLMzfm4pP+8uPSS4orcCIDtoPyYb0ekukaUqF5MOHD2urDjIQ6enp1b6WT0pKkvtT0Tltyk44ASMTMzQJDYew0eNvCYQiYzQJDUfOjXPITjgO9x7DtV6HMnT5FS0R1W2qvsZ525vg61fcMWt3MvYn5qGViymupJWgoLQCX7/izkl7RGpSKiS7u7trqw4yAOnp6QgfG4EycanC8wsXLtRxRY+J87Jg5thU9uZRSSgyhpljU4jzsvRS19Po6itaIqr71HmNG9beFoFNzbH1/9dJHtPRFiM62DIgE2mAQU7cI/3Izc1FmbgUxc1CIDGtXU+mUe4DmCbHa7Uu48YOyLt7GZIyMYSi/95EJGViFGfeg4Vrz6feX58L7WvzK1oiqtuExTkAABMzC+TdSQByUyFsJJKdl5SXoTjjLixbd4GwMAuC0nyFj+Nlb6LWKhaKvkEE9P8tIpG+MSRTNRJTa0gsHGrVtvJFXhsqH9uxuT/S4/bhwcFf0bTbIAgbiSApL8ODUztRUVoMx+cCanwD4UL7RGSozO6cAAA0aVSEtNIipOxZiueeew5GRkaoqKjAzZs3UVFajKaiAphd2a2VGp71DSKgv28RifSNIZkMVuUbiAUAX58WuJYYg5wb52FpaYmCggKUl5fD19cX9smngeTq9+dC+0RkyIq9e0BiZgMA8LJshrvHtiPzUR7M7d1QlJ2MCnEpvHqNgKRlEAoBGOXch2nKBY3WoMo3iIBuvkUk0jeGZDJYT76BWLUG/DpmISsxBuL8h3DwtoNDq2CYWjug8P/bV30DMYSF9omIaiIxs5F9a2fXsT/Mm3VEdsJxiPOy4ODhC3v/EJjaOkPy/+21+c2dMt8garsWIkPBkEwG68k3EAAwtnCAm1tL+TZP/H/VF21DWGifiKi2TG2dDW6lHmXpcw4IkaYxJFO9ZQgL7RORYRGWVJ+gpsn2DRnngFB9w5BM9ZYmF9rnGytR3WZtbQ2RsQlw+7jS9xUZm3Dt8mfgHBCqj9QOyVeuXMGff/6J27dvo6SkBL/99hsAIDk5GRcvXkTXrl1hY2Oj7mWIlKaJhfb5xkpUPzg7OyNqQ2SNS51x7XL1cA4I1UdqheRvvvkG69atg1QqBSC/BbVUKsXMmTMxa9YsvPbaa+pVSaQidRfa5xsrUf3h7Oz81N9Jrl2uOs4BofpI5ZD8+++/Y+3atejVqxemT5+Ov/76Cz///LPsvIeHBwICAnDkyBGGZNIrdRfa5xsrEdHTcQ4I1UfCZzdRbNOmTWjevDmWLVsGHx8fiESiam28vb25Kw8REVE9N7yDLfJLKzB/bypKyh6vO6TqHBAiQ6FyT/KtW7cwbNgwNGpU80M4ODggOztb1UsQERFRHaCJOSBEhkblkGxkZISysqePMcrIyIC5ubmql6A6JKXQSKvtiYjIsKk7B4TI0Kgckn18fHD27FlUVFTAyKh64CkuLsbp06fh5+enVoFUN6y6YqXvEoiISEVVN2MqyclE1tVYiPMfwtjKDg4tg2Bq4yg7LyjNV/g46s4BITIkKofkIUOG4OOPP8b8+fMxb948uXMFBQWYO3cusrKyMHfuXLWLJP0peZSG7IQTEOdlwbixA+z9e8DUtvoL4OTW+XCzqJDdfvCoFHsTHiI1TwzXxsbo728HD9v/ehNSCo0YrImIDITZnROy/09LS8O1a9fQqFEjWFpaIuvuZaRdOAJfX1+4uDAAU8OhckgeOnQozpw5gx07dmDv3r1o3Lix7PitW7dQXFyMQYMGoW/fvhorlnSjskch62os7h7bDiNjU5jbuyHvTgLS4/bBq+cwOLQMAvBfb4KbRQW8rB6H5Kq7Lv2VkI8t5zK46xIRkYEq9u4BiZkNSnIyce3EN3Bo1RlNuw2EsJEIkvIy3Du1E9euxkHUcTBMrR1glHMfpikX9F02kVaptU7yd999h+DgYERFReHGjRuQSqW4fPkymjdvjrFjx2LkyJGaqpN0yOzOCRQVFeFuXBxcXV3RvHlzGBkZoaKZC27evIm7R7fCqfAOzMzMqt2Xuy4REdU9EjMbSCwckHn+CIxMzODRZwLQyBiS/z/v0fsNPLpzGZk3L8G9x/BqwzOI6iO1d9wbPnw4hg8fjpKSEuTm5sLS0hIWFhaaqI30pNi7B+5f/BtGJuZwfeldlDT6b3k/t1ZlyIz8FPfKLOHRcUC13gTuukREVHeJ87Jg5tgUwkbGcseFImOYOTaFOC9LT5UR6Z7aIbmSqakpTE1NNfVwpEcSMxuUFhfCzMkTsHaV9SRUMnPyQmlxISQWDtV6E7jrEhFR3WXc2AF5dy9DUiaGUPRfUJaUiVGceQ8Wrj31VxyRjqm8mUhqairOnDmD4uJi2TGJRIKff/4ZI0eOxLhx43Ds2DFN1Eh6YNzYAcWZ9yApE8sdr3yhNG7soPB+HjYiJKaVyBaTr1S565KHTfVNZ4iIyDDY+/dARWkx7h+Jkr3+S8rEuH8kChWlxbD3D9FzhUS6o3JP8g8//ICjR4/i1KlTsmMrV67EsmXLZLfj4uKwefNmBAQEqFcl6Zy9fw+kx+3D/SNRaBIaDqHIuFYvlMM72GL16SzM35uKT/vLj0nmrktERIbN1NYFnn1eR9KBtci5cQ5mjk1RnHkPFaXF8OzzOkxtnfVd4jOlpKSgoKBA4TlLS0u4ubnpuCKqq1QOyfHx8ejSpYtsO2qpVIqNGzeiWbNmWLt2LTIzMzF+/HisWbMGP/zwg8YKJt1Q9YWSuy4REdVt9n7dYeHug+yE4xDnZcHCtSfs/UPqREDOyclBeHg4JJKqAwUfEwqFiI6Oho2NjW4LozpJ5ZCcnZ0t92ksMTERDx8+xNSpU+Hi4gIXFxe88MILiI2N1UihpHuqvlBy1yUiorrN1NYZ7j2G67sMpdnY2CAqKkrWk5yUlISFCxdi7ty58PT0hKWlJQMy1ZrKIVkikUAqlcpux8bGQiAQoHPnzrJjzs7OyMriTNi6TNUXSu66RERE+qBoOIWnpyd8fHz0UA3VZSqHZDc3N1y6dEl2+9ChQ3B0dESzZs1kxzIzM2WbjBAREZH6UgqNtNqeiB5TOST37t0bq1atwjvvvANjY2OcP38eY8aMkWtz69YteHh4qF0kERFRQydtZAwBpFh1xUrp+5oYi2Btba2FqgxDeno6cnNzqx1PSkqS+7Mqa2trODsb/lhr0g+VQ/KECRPw999/4+DBgwAAX19fTJs2TXY+OTkZly5dwsSJE9WvsgHhrFwiIlJEKjKHFALZ+NonVR17W1V9DoPp6emIGBuOUnHN6/AvXLhQ4XETYxEiN0TV258NqUflkGxpaYlt27bh+vXrACDbuvhJy5Ytg7+/v3oVNiCclUtERM/ytPG1DXHsbW5uLkrFZZjcOh9uFhW1vl9KoRFWXbFCbm4uQzIppPaOezX9Mrq7u8Pd3V3dh29QOCuXiIhINW4WFfCyqn1IJnoWjW1LTZrBWblEREQcfkj6p3JIDgsLq1U7gUCAQ4cOqXoZIiIiamA4/JAMgcoh+ck1kp9UUFCAvLw8AICjo6NsRz4iZQlLqs9UfhqBWHGPAxER1S0cfkiGQOWQfOTIkRrPPXjwAF999RWysrKwbt06VS9BDZS1tTVExibA7eP6LoWIiPSEww9J37QyJtnDwwNLlizBq6++iiVLlmDOnDnauAzVU87OzojaEFnjmpc1LXNUeY6IqC5Q9tsyZdsTkXq0NnFPJBKha9eu2LdvH0PyM3AR9OqcnZ2f+tzYm0BEdZU635aJjE20sikIh7cRVafV1S1KSkoUhj/6T3p6OsLHRqBMXFpjm5p6R0XGJojaEAlnZ2fOAiYiqiNU/bYM0HzniKENb1Ol06imjiQidWktJJ87dw5//fUXvL29tXWJeiE3Nxdl4lIUNwuBxLT2vQPCklzg9nHk5ubCxMSEs4CJiOoQQ/m2zJCGt6nTaaTInexSbIt/hAc5ZfCwEWF4B1t425toolRqIFQOyREREQqPV1RUID09HcnJyQCAKVOmqHqJBkViag2JhYNK9+UsYCIiUpWhBHZVO42Mch/ANDkeKYX/7fq7N+EhFh+8DwsTIzR3NMOxmwVY/XcWPujTBP387ABArj2RIiqH5NjYWIXHBQIBGjdujOeffx7jx4/H888/r3JxVHucBUxERPWBsp1GwuIcAMCqK1YAgKKiIsTF3YerqyuaN28OIyMjBHhW4ObNm/hq/30ceegKMzMzbZRO9YzKIfnq1auarIMMiDITODh5g4io4VK2N1abvbeTW+fDzaICP59IxTVTI6wf4QCTRvmy86UdHTFkdSZa4C4mBroipdBIFqyJFKl1SJ46dSr69++P/v37AwDi4uLg7u7OCWFaUvIoDdkJJyDOy4JxYwfY+/eAqa2LVq9paBM4iBqiJyfhVp2sxEm4ZCisra1hYixSKWSaGIuUWqGjtu+HbhYV8LKqQH5RKdq4msLXVgqgQq6Nn6sp8otK4WVVUe3+RFXVOiQfOnQILVu2lN2OiIjAlClTMHXqVK0U1pBlJ5xA0sF1MDIxg5ljU+TdvYz0uH3w7PM67P26y7V92kxfZZeOq2kCB9cmpoagMpwq+v3RVTitaSveyt8xTsIlQ+Hs7IzIDVFaWaGjcvgEAGRdjcXdY9thZGwKc3s35N1JQHrcPnj1HAaHlkEAAEFpvtz9PWxEOHGrACVlEpiKhLLjJWUSXEkrwZiOtso+XWqgah2SGzdujMLCQtntmralJtVUviiU5GQi6eA6OLQMQtNuAyFsJIKkvAz3Tu1E0oG1sLJzhKm1A4T5aQCkTw2oNZ0zMRYhckNUjUG5phcvjnGm+kpROH3y90dX4bTqJNyqOAmXDIm2JvyZ3TkB4PHY4rtxcXJjiyuaueDmzZu4e3QrnArvKBxbPLyDLVafzsL8van4tL8rTEVClJRJMH9vKgpKKzCiA0My1U6tQ3Lz5s3x559/wt/fH46OjgCA5ORkxMXFPfO+gYGBqlfYQFS+KKTfvo1GRkK0chBBeH2f7HxLexHOGgmRezIK9s2a/f9RgWwMVm1VjsHKzc2ttxuRECnLkMIph1NQQ1fs3QMSMxs8OPsXjEzM4frSuyhpJJKdd2tVhszIT3GvzBIeHQfAKOc+TFMuyM5725vg61fcMWt3MvYn5qGViymupJWgoLQCX7/iDi8uA0e1VOuQPGXKFEyZMgUzZsyQHdu1axd27dr1zPsmJiaqVFxDUvmiUJi8AWZOJij2G1itjdntNBSaWKKw9SuyF4XKMVj0H0OaSEJ1B8MpkWGQmNlAYuGA0uJCmDl5AtauqLoLgJmTF0qLCyGxcJAbnlFpWHtbBDY1x9b/Xyd5TEdbjOhgy4BMSql1SO7WrRv27t2L06dPIz09HcuXL0dgYCCCgoK0WV+DUfmiILJzR+6DYyg3bgyhyPi/82ViFD1MhYNHzxpfFBo6XU4kISIi7TJu7IC8u5chKRNXez8szrwHC9eeT72/l70JZr2o3QnvVL8ptQScu7s7hg0bBgBYvnw5goKCOHFPw+z9eyA9bh/uH4lCk9BwCEXGkJSJcf9IFCpKi2HvH6LvEg2WNieSEBGRbvH9kPRN5SXgvvzyS7Rq1UprhTVUprYu8OzzOpIOrEXOjXMwc2yK4sx7qCgthmef12FqyyD3NIaycxQRYBgrZhDVVXw/JH1TeQm4OXPmYMqUKXLHSDPs/brDwt0H2QnHIc7LgoVrT9j7h/AFgagOMZQVMwDV117mms2kb3w/JH3iEnAGytTWGe49hit9vzvZpdj2/xMVPGxEGN7BFt6cqECkc4ayYoaqay9zzWbSl6q7vpobG8G8Y6h8o8Is2f9y51fSFi4BV49su/AIs3cnw8rECK1cTHHiVgFWn87C16+4Y1h7rgtJpGuG0NOqalg3lJBPDQd3fSVDwyXgDETVT87PUvWT853sUszenYzh7W2xoJ/84umzdicjsKk5l74hg8Mxu7qh6s+RP3/SpZp2fQW48yvpB5eA0zNNfXLeFv8IViZGsoAMAKYiIT7t74p9iXnYGv+IS+GQQTGkMbtEZBg4+ZoMCZeA0zNNfXJ+kFOGVi6mcvvUA4+DcmsXUzzIKdPOEyBSkba+zmfvNGkb/40RNQxKheQnHT58GI0bN9ZkLQ2WJj45e9iIcOJWAUrKJHJBuaRMgitpJRjTkWOSSXtUXQVB02GCvdOkbfw3Zri42yppmsoh2d3dXZN1kIoqf8mf93XA6r+z8P4faXg31B0mIiFKyyT44UgaCkoq8LyvA+7mG/FFgTTOkFZB4GQz0jb+GzM83G2VtKXWIXn58uUQCAQYM2YMbGxssHz58lrdTyAQYMqUKSoXqClisRg//PAD/vjjD+Tl5cHX1xfvvfcenn/+eX2XppYnXxSe85Hir4RrOJCYB0tLSxQUFKC8vBy+vr74+SbXlCTtMLTQwK+6DVt9WHu5LtTYkHC3VdIWpUNy//7962RInj17Ng4cOICIiAh4eXlh586dmDhxItavX49OnTrpuzyVTW6dDzeLiv+/ZYoH3Vpib8JDpOaJ4drYDv397eBhawIgB8DjnmdVPm2TerQRDAwpbDA0UG2o862DIf17J8PDCX+kDbUOyZGRkQD+ezOsvF0XXLp0CX/99Rc+/PBDTJgwAQAwcOBAvPTSS1i8eDG2bNmi5wpV52ZRAS+rCtltL6tG6NbUqUqrCmhbQxoLVtOknZreqLUxHMGQhjgQ1Zaq3zrw3zsR6UOtQ3LVpd7q0tJv+/fvh5GREUaMGCE7ZmJigqFDh+L7779HamoqXF1d9Vhh3dXQxoI9bdJOTW/U2hiOYGhDHIhqS5UeX66EQkT6oPLEvbokMTERXl5esLS0lDseEBAgO8+QrJqGNhbsaW/WT3uj1sabLd/AqSHhSihEpGsqh+T09HQcOnQICQkJePToEQDAzs4Ofn5+ePHFF+HkVPUrf/3JzMyUbaX9pMpjGRkZCu+XkZGBzMxMheeKioo0V+ATlB13Vzls4WGJECUVAoWPaWokhZ2pRK69MrU8q5el6liwJ59DVeqOv1W2x0cbPUV1PZxybCeR4X0bYyivcaq+PhjSvAu+xpGmCKRSqVTZO/3444/49ddfUVZWhqp3FwgEMDY2xsSJEw1iwh4AvPDCC/D29sYvv/wid/z+/ft44YUX8NFHH2HcuHHV7rds2bJnTlA8f/58tR5qVeXk5GDw4MHVxt1VerJnIz09HRFjw1EqVn6TEBNjESI3RD21F1eZWjRxv6cxpFrqOv5MiAyPobzGGUodhlYLNVxKh+QlS5Zg9erVMDY2Rr9+/RAUFCTrNc7IyEBMTAz279+PsrIyvP3225g2bZpWClfGSy+9BHt7e6xfv17u+M2bNzFgwAB8+umnGDlyZLX7PasnOTw8XKMhGVCuFzY9PV02zCEzM7PG3m1zc3O5nvTaDnNQtUdYmz3JhlBLXcefCZHhMZTXOEOpw9BqoYZJqZB8//599O3bF66urvjll1/g7e2tsN2dO3cwYcIEZGRkYN++fWjSpInGClbF+PHjkZ6ejr1798odP3PmDMaNG4eVK1ciNDRUqccsKChAx44dNR6SiYiIiEj/hM9u8p+dO3dCIpHgm2++qTEgA4C3tze+/fZblJeX448//lC7SHW1bNkSd+/erfbJ8uLFiwCAVq1a6aMsIiIiIjJQSoXk+Ph4tGjRAh06dHhm244dO8LHxwfnzp1TuThN6du3LyoqKrB161bZMbFYjOjoaLRt25YrWxARERGRHKVWt7h16xZCQkJq3T4gIAAnTpxQuihNa9u2Lfr27Yvvv/8e2dnZ8PT0xM6dO5GcnCy35A8REREREaBkSM7Pz4e9vX2t29vb2yM/P1/porThm2++wdKlS7F7927k5ubC19cXq1atQmBgoL5LIyIiIiIDo1RILikpgUgkqnV7kUiEkpISpYvSBhMTE8yaNQuzZs3SdylEREREZOCUGpNMRERERNQQKL3j3p49e2SrQjzLvXv3lC6IiIiIiEjflA7JSUlJcttePotAoHirZCIiIiIiQ6VUSD58+LC26iAiIiIiMhhKhWR3d3dt1UFEREREZDA4cY+IiIiIqAqGZCIiIiKiKhiSiYiIiIiqYEgmIiIiIqqCIZmIiIiIqAqBVCqV6ruIukgqlaKwsBAWFhZcC5qIiIionmFIJiIiIiKqgsMtiIiIiIiqYEgmIiIiIqqCIZmIiIiIqAqGZCIiIiKiKhiSiYiIiIiqYEgmIiIiIqqCIZmIiIiIqAqGZCIiIiKiKhiSiYiIiIiqYEgmIiIiIqqikb4LaGikUikKCwv1XQYRERFVYWFhAYFAoO8yyEAwJOtYYWEhOnbsqO8yiIiIqIrz58/D0tJS32WQgRBIpVKpvotoSDTVk5yYmIjw8HBERUWhVatWGqisbtfBWgy/FkOpg7WwlrpYB2vRTS3sSaYnsSdZxwQCgUY+pZqbm8v+1OenXkOpg7UYfi2GUgdrYS11sQ7WUjdqofqFE/eIiIiIiKpgSCYiIiIiqoIhmYiIiIioCoZkIiIiIqIqGJKJiIiIiKpgSK6jHB0dMXXqVDg6OrIO1lInajGUOlgLa6mLdbCWulEL1S9cJ5mIiIiIqAr2JBMRERERVcGQTERERERUBUMyEREREVEVDMlERERERFU00ncBpLx///0Xy5YtQ3x8PEpLS9GkSRMMHz4cEREROqth9uzZ2LlzZ43nT5w4AWdnZ53UcvfuXfzwww84f/48cnNz4erqipdeegkTJkyAmZmZTmqodPnyZSxZsgQXLlyAVCpF+/bt8cEHH6BVq1Zau2ZhYSHWrFmDixcvIiEhAbm5ufjyyy8xePDgam1v3bqFRYsWIT4+HiKRCCEhIfjoo49gZ2en01ouXbqE6OhoXLp0CdeuXUN5eTmuXbumkRpqW4dEIsGuXbtw8OBBJCYmIjc3Fx4eHujfvz8mTJgAExMTndUCANu2bcPu3btx+/Zt5OXlwcnJCcHBwZgyZQo8PDx0WsuTysrK8Oqrr+LWrVv48MMPMWHCBJ3WUtNrjbe3N/bv36/TWoDH/262bNmCrVu34s6dOzAzM4Ovry/mzJmDli1b6qQOX1/fGh+ja9euWLdunVp1KFMLAOzduxe//fYbbt++DSMjI7Ro0QJvvPEGevbsqXYdytYSFRWFjRs34v79+7C1tUX//v3x7rvvwtzcXCO1UMPCkFzHnDp1CpMnT0br1q3x9ttvw9zcHPfu3UNaWppO6xgxYgS6dOkid0wqlWLBggVwd3fXWUBOTU3FsGHDYGVlhfDwcFhbW+Off/7BsmXL8O+//2LlypU6qQN4/OFl9OjRcHV1xdSpUyGRSLBp0yaEh4dj+/btaNasmVau++jRI6xYsQJubm7w9fVFbGyswnZpaWkYM2YMrKysMH36dBQVFWHt2rW4fv06tm/fDmNjY53Vcvz4cezYsQM+Pj7w8PDA3bt31b62snUUFxfjo48+Qrt27TBy5EjY29vjwoULWLZsGc6cOYPIyEgIBAKd1AIAV65cgYeHB0JDQ9G4cWM8ePAA27dvx9GjR/HHH39o5HeqtrU8KSoqCqmpqWpfW51ajI2N8cUXX8gds7Ky0kstc+bMwZ49e/Dqq68iPDwcRUVFSExMRHZ2ts7q+Oabb6odu3z5MiIjI/H888+rXYcytWzYsAFffPEFevbsiRkzZqC0tBQ7d+7EpEmTsGzZMvTu3VtntXz77bf49ddf0adPH0RERODWrVuIiorCzZs3sWbNGrXroAZISnVGfn6+tGvXrtIpU6ZIKyoq9F1ONXFxcVIfHx/pypUrdXbNlStXSn18fKTXr1+XO/7hhx9KfXx8pDk5OTqr5c0335QGBgZKHz58KDuWnp4ubdeunXTq1Klau25paak0IyNDKpVKpZcuXZL6+PhIf//992rt5s+fLw0ICJAmJyfLjv39999SHx8f6ZYtW3RaS2ZmprS4uFgqlUqln376qdTHx0cj11emjtLSUun58+er3XfZsmVSHx8f6d9//62zWmqSkJAg9fHxka5evVovtWRlZUk7duwoXb58udTHx0f666+/aqQOZWqZNWuWtF27dhq7rjq1/PXXX1IfHx/pwYMH9VqHInPmzJH6+vpKU1NTdVpL7969pUOGDJFKJBLZsfz8fGm7du2kkydP1lkt6enp0tatW0s/+OADueMbNmyQ+vj4SA8fPqyRWqhh4ZjkOmTPnj3IysrC9OnTIRQKUVRUBIlEou+yZP78808IBAK89NJLOrtmQUEBAMDe3l7uuKOjI4RCIUQikc5qOXfuHLp06QJbW1vZMScnJwQFBeHo0aMoLCzUynWNjY1rtYj+wYMH0bNnT7i5ucmOde3aFV5eXti3b59Oa3FwcICpqalGrqlqHcbGxujQoUO14y+++CKAx0NTdFVLTdzd3QEAeXl5eqll8eLF8Pb2xiuvvKKR66tTS0VFhez3XV+1/PbbbwgICMCLL74IiUSCoqIivdRRlVgsxsGDBxEYGAgXFxed1lJQUAB7e3u5b10sLS1hYWGhsd/x2tTyzz//oLy8HAMGDJA73r9/fwDAX3/9pZFaqGFhSK5Dzpw5A0tLS6Snp6NPnz5o3749OnbsiPnz56O0tFSvtZWVlWHfvn1o3769xsZP1kZQUBAAYO7cuUhMTERqair27t2LzZs3Y+zYsTodhyYWixW+KZiamqKsrAw3btzQWS1VpaenIzs7G35+ftXOBQQEIDExUQ9VGaasrCwAkPuwo0uPHj1CdnY2EhIS8NFHHwFAtaFNunDp0iXs2rULc+bM0ciwE3UUFxejY8eO6NixI4KCgvDpp59q7UNnTQoKCnDp0iX4+/vj+++/R8eOHdG+fXuEhYVh7969Oq2lquPHjyMvL08rH2aeJSgoCCdPnsSGDRvw4MED3Lp1C59++iny8/N1Ok9GLBYDQLW5BJXzUv7991+d1UL1B8ck1yF3795FRUUF3n77bQwdOhQzZsxAbGwsNmzYgPz8fHz//fd6q+3UqVPIycnByy+/rNPr9ujRA++++y5Wr16NI0eOyI5PnjwZ06dP12kt3t7e+Oeff1BRUQEjIyMAj1+4L126BOBxUNWXjIwMAFDYG+Po6IicnByIxWKNjEuu63799VdYWlqiR48eerl+jx49ZG/4NjY2+PjjjzU2zrS2pFIpPv/8c/Tv3x/t27fHgwcPdHr9Jzk6OuKNN95A69atIZVKcfLkSWzatAlXr17Fhg0b0KiRbt7G7t27B6lUir/++guNGjXCBx98ACsrK0RGRuL999/X67+ZPXv2wNjYGH369NH5tT/++GM8evQIX3zxhWzcuK2tLX777Te0b99eZ3V4e3sDAOLj49G5c2fZ8XPnzgHQ7+sv1V0MyXVIUVERiouLMXLkSHz88ccAgN69e0MsFmPr1q1455134OXlpZfa/vzzT4hEIvTr10/n13Z3d0enTp3Qp08f2NjY4NixY1i9ejUcHR0RHh6uszpGjx6NBQsWYO7cuXjjjTcgkUiwcuVKZGZmAgBKSkp0VktVld80KArBlT0vJSUlDT4kr1q1CqdPn8b8+fPRuHFjvdTwyy+/oLS0FLdv38bu3btRXFys8xqio6Nx/fp1/Pjjjzq/dlUzZsyQuz1gwAB4eXlhyZIlOHDgQLWv17WlcmhFTk4Otm3bhrZt2wIAQkNDERYWhpUrV+olJBcUFODYsWMICQnRy79ZU1NTeHt7w8XFBT179kRhYSF+++03TJs2DRs3boSnp6dO6mjTpg3atm2LX375Bc7OzggODpb1aotEIr1/20p1E0NyHVL5VX7VMb8vv/wytm7din/++UcvIbmwsBCHDx9Gt27ddP4V9V9//YV58+bhwIEDsrF4vXv3hlQqxeLFizFgwACd1TRq1CikpaVhzZo1siWr/Pz8MGHCBKxatQoWFhY6qUORyiBc2UP5pMo3D22OEa4L9u7di6VLl2Lo0KEYPXq03uqo7AULCQlBWFgYXnrpJZibm+vsA19BQQG+//57TJgwAa6urjq5prLGjRuHH374AadPn9ZZSK78HfLw8JAFZACwsLBAr169sGfPHpSXl+usZ7vSgQMHUFpaqvNv8Sq9++67aNSoEVatWiU7FhYWhj59+mDJkiVYunSpzmpZtmwZ3nvvPcyZMwcAYGRkhHHjxiEuLg537tzRWR1Uf3BMch3i5OQEoPoktco1bnNzc3VeEwAcOnQIxcXFenmR3rRpE1q1alVtskpoaCiKi4t1PtZ2+vTp+Pvvv7Fx40bs3r0bv//+O6RSKQDorZcf+O/fTmWv9pMyMzNhY2PToHuR//77b3z44Yfo2bMnPv30U32XI9O0aVO0bt0ae/bs0dk116xZg7KyMvTv3x8PHjzAgwcPZEtM5uXl4cGDBwo/bOmSqakpbGxsdPqaV/k75ODgUO2cvb09ysrK9NLrv2fPHlhZWaFXr146v/b9+/dx8uRJhIaGyh23sbFBhw4dEB8fr9N6nJ2dsXnzZhw4cAAbN27E8ePH8eGHHyI1NVWvr79Ud7EnuQ5p06YN/v77b6Snp8utuVs53lRTG0Ioa8+ePTA3N6/2QqkLWVlZsLa2rna8rKwMAFBeXq7rkmBtbY1OnTrJbp8+fRouLi5aWye5NpydnWFnZ4fLly9XO3fp0iW1N0Goyy5evIipU6fCz88PS5cu1XlP4LOUlJToNJSmpqYiNzdXYQ/tqlWrsGrVKuzatUurG+Q8S0FBAR49eqTT1zxnZ2c4OjoqHNuakZEBExMTnX9blJGRgZiYGAwaNEgvH3IrJ7lWVFRUO1deXq7wuC54eXnJQvHNmzeRmZn51A1ziGrCnuQ6pHK8744dO+SO79ixA40aNZKt9KBLDx8+xJkzZ/Diiy/qfHc74PFkjStXrlT7Ku2vv/6CUCh86s5UurB3714kJCTgtddeg1Co31+33r1749ixY3IbQ5w5cwZ3795F37599ViZ/ty6dQsTJ06Eu7s7Vq9erbchJ+Xl5Qp7RS9duoTr168rXJVEW8aOHYsVK1bI/ffZZ58BAAYPHowVK1bobAWb0tJShcu+/fTTT5BKpejevbtO6qjUr18/pKam4u+//5Yde/jwIQ4fPozOnTvr/Hd87969kEgkehtq4enpCaFQiL1798q+MQMeb1x07tw5vX6QAh7vjvjtt9/CzMwMI0eO1GstVDcZVpcJPVXr1q0xZMgQ/P7776ioqEBgYCBiY2Oxf/9+TJo0SWe73D1p7969KC8v19uL9IQJE3DixAmMGTMGY8aMkU3cO3HiBIYNG6bTn0lcXBxWrFiB559/HjY2Nrh48SKio6PRvXt3rS+FFBUVhby8PNm3CkePHpV9RT527FhYWVlh8uTJ2L9/PyIiIhAREYGioiKsWbMGPj4+GDJkiE5rSU5Oxh9//AEAst7tn376CQDg5uaGgQMHar0OgUCACRMmIC8vDxMmTMCxY8fk7t+0aVONzc5/Vi1SqRQ9e/ZEv3790KJFC5iZmeH69euIjo6GlZUV3n77bY3UUZta2rRpgzZt2sjdp3J1i+eeew4vvPCCzmrJzc3FoEGDMGDAANk3MadOncLx48fRvXt3hIWF6awWKysrTJo0Cfv27cO0adMwfvx4WFlZYfPmzSgvL8f777+vszoq7d69W7Z9uTY8qxY7OzsMGTIE27dvx2uvvYbevXujsLAQmzZtQmlpKSZNmqSzWqysrPDFF19ALBajZcuWKC8vx59//olLly7hq6++klsfnqi2BNInP/6RwSsrK8Pq1asRHR2NjIwMuLm5YfTo0Rg3bpxe6hkxYoRsXFrlsme6dunSJSxbtgyJiYnIycmBu7s7Bg0ahDfeeEOnX53fu3cPn376Kf79918UFhbCw8MDgwYNwrhx47T+VWhoaCiSk5MVnjt8+LCs5+/GjRv46quvcP78eYhEIoSEhGD27NkKx1lqs5aYmJgaPzgEBQVhw4YNWq8DwFND1qBBg/DVV1+pXUdtanFycsK3336LmJgYJCcno7S0FE5OTujSpQveeustjfbc1vbfypMePHiAsLAwfPjhh5gwYYLOamncuDE+//xzXLx4ERkZGaioqICnpydefvllvP766xrdLKi2P5f79+/j66+/xpkzZ1BeXo527dphxowZCAgI0Gkdt2/fRr9+/TB+/HjMnj1bI9dWpZby8nJs2bIFO3bsQFJSEgDA398fb7/9ttxSbLqoJTo6GuvXr8e9e/cgEAgQEBCAyZMna7QOalgYkomIiIiIquCYZCIiIiKiKhiSiYiIiIiqYEgmIiIiIqqCIZmIiIiIqAqGZCIiIiKiKhiSiYiIiIiqYEgmIiIiIqqCIZmIiIiIqAqGZCIiIiKiKhiSiYiIiIiqYEgmogbhwYMH8PX1xYQJE2psExMTA19fX8ybN0+HlRERkSFiSCYiIiIiqoIhmYiIiIioCoZkIqJnSE5Oxpw5c9C9e3f4+fmhR48emDNnDlJSUqq1DQ0NRWhoqMLHGTt2LHx9feWOLVu2DL6+voiJiUF0dDQGDRqEtm3bYuzYsVp5LkREVDuN9F0AEZEhu3PnDkaPHo2HDx+iV69eaNGiBW7cuIHff/8dR48exaZNm+Dt7a32ddasWYOYmBiEhYXh+eefh5GRkQaqJyIiVTEkE1GDcu/ePSxbtkzhueTk5GrH5s+fj4cPH+Kzzz7DiBEjZMc3btyIzz77DAsWLMD69evVrisuLg7btm2r1tNMRET6wZBMRA3KvXv3sHz58lq1TUlJQUxMDJ577jkMHz5c7tyoUaMQFRWFs2fPIjU1Fa6urmrVNXz4cAZkIiIDwpBMRA1Kt27dsGbNGoXnYmJiEBERIbudmJgIAAgMDIRAIJBrKxQKERgYiNu3byMxMVHtkBwQEKDW/YmISLM4cY+IqAYFBQUAAAcHB4XnHR0d5dqpw97eXu3HICIizWFIJiKqgaWlJQAgKytL4fnMzEy5dgAgEAhQXl6usH1+fn6N16raU01ERPrFkExEVINWrVoBAM6dOwepVCp3TiqV4ty5c3LtAMDa2hoPHz6sFpSLioqQlJSk5YqJiEhTGJKJiGrg5uaG4OBg3LhxAzt27JA7t3XrVty6dQudO3eWG4/s5+eHsrIy7NmzR3ZMKpXi+++/R1FRkc5qJyIi9XDiHhHRUyxYsACjR4/GJ598gqNHj+K5557DjRs3cOTIEdjZ2WHBggVy7cPDwxEdHY2PP/4Yf//9N+zs7HDu3Dnk5+ejZcuWuHr1qn6eCBERKYU9yURET9GsWTP8/vvvGDRoEC5duoQ1a9YgISEBgwcPxo4dO6ptJOLj44Nff/0Vbdq0wYEDB/DHH3/gueeew5YtW9C4cWM9PQsiIlKWQFp1oB0RERERUQPHnmQiIiIioioYkomIiIiIqmBIJiIiIiKqgiGZiIiIiKgKhmQiIiIioioYkomIiIiIqmBIJiIiIiKqgiGZiIiIiKgKhmQiIiIioioYkomIiIiIqmBIJiIiIiKqgiGZiIiIiKgKhmQiIiIioir+D2auiiytGKXuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          6     7    8    9    10   11   12   13   14   15   16   17   18   19\n",
       "p-value  0.0  0.22  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "variable = 'DHI'\n",
    "label = 'Diffuse Horizontal Irradiance (W m-2)'\n",
    "\n",
    "nsrdb = solar_comp.loc[solar_comp['dataset'] == 'NSRDB', variable]\n",
    "wtk = solar_comp.loc[solar_comp['dataset'] == 'WTK', variable]\n",
    "fig, ax = plt.subplots(figsize=((6,5)), dpi=100)\n",
    "sns.distplot(nsrdb, bins=100, ax=ax, kde=False, norm_hist=True)\n",
    "sns.distplot(wtk, bins=100, ax=ax, kde=False, norm_hist=True)\n",
    "sns.despine(offset=10, trim=False)\n",
    "plt.ylabel('Probability')\n",
    "plt.xlabel(label)\n",
    "plt.legend(['NSRDB', 'WTK'])\n",
    "plt.show()\n",
    "\n",
    "p_val = wilcoxon(nsrdb, wtk)[1]\n",
    "print('p-value = {:.2f}'.format(p_val))\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Annual Statistics', fontsize=16)\n",
    "sns.boxplot(x='year', y=variable, hue='dataset', data=solar_comp,\n",
    "            showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Year', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = solar_comp.groupby('year')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Monthly Statistics', fontsize=16)\n",
    "sns.boxplot(x='month', y=variable, hue='dataset', data=solar_comp,\n",
    "            showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Month', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = solar_comp.groupby('month')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Diurnal Statistics', fontsize=16)\n",
    "sns.boxplot(x='hour', y=variable, hue='dataset', data=solar_comp,\n",
    "           showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Hour', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = solar_comp.groupby('hour')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take aways:\n",
    "- The NSRDB reports greather DHI than the WTK.\n",
    "- The variability of DHI is also larger in the NSRDB than the WTK.\n",
    "- The monthly pattern of DHI is not well correlated between the NSRDB and WTK."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Direct Normal Irradiance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:57:10.356790Z",
     "start_time": "2019-02-07T02:57:07.684120Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5965/2591778261.py:7: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(nsrdb, bins=100, ax=ax, kde=False, norm_hist=True)\n",
      "/tmp/ipykernel_5965/2591778261.py:8: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(wtk, bins=100, ax=ax, kde=False, norm_hist=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value = 0.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         2007  2008  2009  2010  2011  2012  2013\n",
       "p-value   0.0  0.61  0.03  0.09  0.06   0.3  0.51"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1    2    3    4    5    6    7    8    9    10   11   12\n",
       "p-value  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          6    7    8     9    10   11   12   13   14   15   16   17   18   19\n",
       "p-value  0.0  0.0  0.0  0.07  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "variable = 'DNI'\n",
    "label = 'Direct Normal Irradiance (W m-2)'\n",
    "\n",
    "nsrdb = solar_comp.loc[solar_comp['dataset'] == 'NSRDB', variable]\n",
    "wtk = solar_comp.loc[solar_comp['dataset'] == 'WTK', variable]\n",
    "fig, ax = plt.subplots(figsize=((6,5)), dpi=100)\n",
    "sns.distplot(nsrdb, bins=100, ax=ax, kde=False, norm_hist=True)\n",
    "sns.distplot(wtk, bins=100, ax=ax, kde=False, norm_hist=True)\n",
    "sns.despine(offset=10, trim=False)\n",
    "plt.ylabel('Probability')\n",
    "plt.xlabel(label)\n",
    "plt.legend(['NSRDB', 'WTK'])\n",
    "plt.show()\n",
    "\n",
    "p_val = wilcoxon(nsrdb, wtk)[1]\n",
    "print('p-value = {:.2f}'.format(p_val))\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Annual Statistics', fontsize=16)\n",
    "sns.boxplot(x='year', y=variable, hue='dataset', data=solar_comp,\n",
    "            showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Year', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = solar_comp.groupby('year')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Monthly Statistics', fontsize=16)\n",
    "sns.boxplot(x='month', y=variable, hue='dataset', data=solar_comp,\n",
    "            showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Month', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = solar_comp.groupby('month')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Diurnal Statistics', fontsize=16)\n",
    "sns.boxplot(x='hour', y=variable, hue='dataset', data=solar_comp,\n",
    "           showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Hour', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = solar_comp.groupby('hour')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take aways:\n",
    "- The average DNI is slightly higher in the WTK on an annual basis.\n",
    "- The median DNI is substantially higher in the WTK on an annual and monthly basis.\n",
    "- The variability of DNI is comparable between the NSRDB and WTK on an annuaul and monthly basis. \n",
    "- The montly pattern of DNI does not match up well between NSRDB and WTK.\n",
    "- Of significant note is the fact that DNI varies little in the WTK during the middle of the day,  \n",
    "  when the variability is greatest in the NSRDB."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions for Irradiance Variables\n",
    "\n",
    "The discrepencies in irradiance values between NSRDB and WTK can be attributed to poor modeling of  \n",
    "clouds in the underlying WRF model used to derive the WTK.\n",
    "- WRF appears to underpredict the total cloud over.\n",
    "    - A known issue with WRF is the ability to represent thin clouds.\n",
    "- This is consistent with the higher GHI (and DNI), but lower DHI values\n",
    "- This also explains the lack of mid-day variability in DHI in the WTK data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Meterological Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Temperature](#Surface-Temperature) | [Wind Speed](#Surface-Wind-Speed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Surface Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:57:13.211849Z",
     "start_time": "2019-02-07T02:57:10.358249Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5965/2950146209.py:7: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(nsrdb, bins=100, ax=ax, kde=False, norm_hist=True)\n",
      "/tmp/ipykernel_5965/2950146209.py:8: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(wtk, bins=100, ax=ax, kde=False, norm_hist=True)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value = 0.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         2007  2008  2009  2010  2011  2012  2013\n",
       "p-value   0.0   0.0   0.0   0.0   0.0   0.0   0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1    2    3    4    5    6    7    8    9    10   11   12\n",
       "p-value  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0    1    2    3    4    5    6    7    8    9    10   11   12   13  \\\n",
       "p-value  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "          14    15   16   17   18   19   20   21   22   23  \n",
       "p-value  0.0  0.59  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "variable = 'Temperature'\n",
    "label = 'Temperature (C)'\n",
    "\n",
    "nsrdb = comp_ds.loc[comp_ds['dataset'] == 'NSRDB', variable]\n",
    "wtk = comp_ds.loc[comp_ds['dataset'] == 'WTK', variable]\n",
    "fig, ax = plt.subplots(figsize=((6,5)), dpi=100)\n",
    "sns.distplot(nsrdb, bins=100, ax=ax, kde=False, norm_hist=True)\n",
    "sns.distplot(wtk, bins=100, ax=ax, kde=False, norm_hist=True)\n",
    "sns.despine(offset=10, trim=False)\n",
    "plt.ylabel('Probability')\n",
    "plt.xlabel(label)\n",
    "plt.legend(['NSRDB', 'WTK'])\n",
    "plt.show()\n",
    "\n",
    "p_val = wilcoxon(nsrdb, wtk)[1]\n",
    "print('p-value = {:.2f}'.format(p_val))\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Annual Statistics', fontsize=16)\n",
    "sns.boxplot(x='year', y=variable, hue='dataset', data=comp_ds,\n",
    "            showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Year', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = comp_ds.groupby('year')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Monthly Statistics', fontsize=16)\n",
    "sns.boxplot(x='month', y=variable, hue='dataset', data=comp_ds,\n",
    "            showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Month', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = comp_ds.groupby('month')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Diurnal Statistics', fontsize=16)\n",
    "sns.boxplot(x='hour', y=variable, hue='dataset', data=comp_ds,\n",
    "           showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Hour', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = comp_ds.groupby('hour')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "with pd.option_context('display.max_rows', None, 'display.max_columns', 24):\n",
    "    display(p_vals.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take aways:\n",
    "- The average Temperature is slightly higher in the WTK\n",
    "- The variability of Temperature is comparable between the NSRDB and the WTK,  \n",
    "  though the distribution of tempertures in the WTK is slightly offest from the NSRDB.\n",
    "- The WTK suggests that it is slightly warmer in the mornings and evenings but shows the same  \n",
    "  diurnal pattern as the NSRDB."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Surface Wind Speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:57:15.967446Z",
     "start_time": "2019-02-07T02:57:13.213198Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5965/399998482.py:7: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(nsrdb, bins=100, ax=ax, kde=False, norm_hist=True)\n",
      "/tmp/ipykernel_5965/399998482.py:8: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(wtk, bins=100, ax=ax, kde=False, norm_hist=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value = 0.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         2007  2008  2009  2010  2011  2012  2013\n",
       "p-value   0.0   0.0   0.0   0.0   0.0   0.0   0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1    2    3    4    5    6    7    8    9    10   11   12\n",
       "p-value  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0    1    2    3    4    5     6    7    8    9    10   11   12  \\\n",
       "p-value  0.0  0.0  0.0  0.0  0.0  0.0  0.11  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "          13    14   15   16   17   18   19   20   21   22   23  \n",
       "p-value  0.0  0.09  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "variable = 'Wind Speed'\n",
    "label = 'Wind Speed (m/s)'\n",
    "\n",
    "nsrdb = comp_ds.loc[comp_ds['dataset'] == 'NSRDB', variable]\n",
    "wtk = comp_ds.loc[comp_ds['dataset'] == 'WTK', variable]\n",
    "fig, ax = plt.subplots(figsize=((6,5)), dpi=100)\n",
    "sns.distplot(nsrdb, bins=100, ax=ax, kde=False, norm_hist=True)\n",
    "sns.distplot(wtk, bins=100, ax=ax, kde=False, norm_hist=True)\n",
    "sns.despine(offset=10, trim=False)\n",
    "plt.ylabel('Probability')\n",
    "plt.xlabel(label)\n",
    "plt.legend(['NSRDB', 'WTK'])\n",
    "plt.show()\n",
    "\n",
    "p_val = wilcoxon(nsrdb, wtk)[1]\n",
    "print('p-value = {:.2f}'.format(p_val))\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Annual Statistics', fontsize=16)\n",
    "sns.boxplot(x='year', y=variable, hue='dataset', data=comp_ds,\n",
    "            showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Year', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = comp_ds.groupby('year')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Monthly Statistics', fontsize=16)\n",
    "sns.boxplot(x='month', y=variable, hue='dataset', data=comp_ds,\n",
    "            showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Month', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = comp_ds.groupby('month')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "display(p_vals.round(2))\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(6,5), dpi=100)\n",
    "axis = fig.add_subplot(111)\n",
    "axis.set_title('Diurnal Statistics', fontsize=16)\n",
    "sns.boxplot(x='hour', y=variable, hue='dataset', data=comp_ds,\n",
    "           showfliers=False, ax=axis, showmeans=True, meanprops=meanprops)\n",
    "sns.despine(offset=10, trim=False)\n",
    "axis.set_xlabel('Hour', fontsize=14)\n",
    "axis.set_ylabel(label, fontsize=14)\n",
    "axis.tick_params(axis='x', labelsize=12, width=1, length=6)\n",
    "axis.tick_params(axis='y', labelsize=12, width=1, length=6)\n",
    "fig.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "p_vals = pd.DataFrame()\n",
    "groups = comp_ds.groupby('hour')\n",
    "for group, df in groups:\n",
    "    nsrdb = df.loc[df['dataset'] == 'NSRDB', variable].values\n",
    "    wtk = df.loc[df['dataset'] == 'WTK', variable].values\n",
    "    p_vals.loc['p-value', group] = wilcoxon(nsrdb, wtk)[1]\n",
    "    \n",
    "with pd.option_context('display.max_rows', None, 'display.max_columns', 24):\n",
    "    display(p_vals.round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take aways:\n",
    "- The average Wind Speed is slightly higher in the WTK.\n",
    "- But, the WTK and NSRDB indicate the same variability in the wind speed.\n",
    "- The diurnal patters are poorly correlated between WTK and NSRDB.  \n",
    "  The NSRDB indicated it is windiest during midday, while in the WTK  \n",
    "  it is windiest in the evening."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions for Meteorological Variables\n",
    "\n",
    "The NSRDB's Temperature and Wind Speed is pulled from spatially coarse re-analysis data (MERRA).  \n",
    "The WTK is also derived from coarse re-analysis data (ERA-interim),  \n",
    "but is then resolved to a much high spatial resolution using the Weather Research and Forecasting Model (WRF).\n",
    "\n",
    "As temperature is not expected to vary significantly spatially it is expected that the NSRDB and WTK temperatures  \n",
    "should match up closely, which is indeed the case.  \n",
    "- The slight differences are likley due to differences in the underlying re-analysis data.\n",
    "\n",
    "Conversely wind speed does vary substantially over small spatial length scales and therefore it is unsurpising  \n",
    "that the NSRDB and WTK measurements for a given site differ.\n",
    "- Smoothing over the coarser grid would account for the lower average annual and monthly values in the NSRDB.\n",
    "- Differences in the dirunal pattern is likely due to spatial discrepencies in the source data,  \n",
    "  i.e. the WTK pixel is not coincident with the MERRA pixel used by the NSRDB."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Statistical Significance\n",
    "Two sided p-values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:57:16.160539Z",
     "start_time": "2019-02-07T02:57:15.969057Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GHI</th>\n",
       "      <th>DHI</th>\n",
       "      <th>DNI</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Wind Speed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual Means</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              GHI   DHI   DNI  Temperature  Wind Speed\n",
       "year                                                  \n",
       "2007          0.0  0.00  0.00          0.0         0.0\n",
       "2008          0.0  0.00  0.61          0.0         0.0\n",
       "2009          0.0  0.17  0.03          0.0         0.0\n",
       "2010          0.0  0.10  0.09          0.0         0.0\n",
       "2011          0.0  0.00  0.06          0.0         0.0\n",
       "2012          0.0  0.00  0.30          0.0         0.0\n",
       "2013          0.0  0.00  0.51          0.0         0.0\n",
       "Annual Means  0.0  0.00  0.00          0.0         0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_stats = pd.DataFrame()\n",
    "groups = solar_comp.groupby('year')\n",
    "for group, df in groups:\n",
    "    for var in ['GHI', 'DHI', 'DNI']:\n",
    "        nsrdb = df.loc[df['dataset'] == 'NSRDB', var].values\n",
    "        wtk = df.loc[df['dataset'] == 'WTK', var].values\n",
    "        annual_stats.loc[group, var] = wilcoxon(nsrdb, wtk)[1]\n",
    "\n",
    "annual_means = solar_comp.groupby(['dataset', 'year']).mean()\n",
    "for var in ['GHI', 'DHI', 'DNI']:\n",
    "    nsrdb = annual_means.loc['NSRDB'][var].values\n",
    "    wtk = annual_means.loc['WTK'][var].values\n",
    "    annual_stats.loc['Annual Means', var] = ttest_rel(nsrdb, wtk)[1]\n",
    "\n",
    "\n",
    "groups = comp_ds.groupby('year')\n",
    "for group, df in groups:\n",
    "    for var in ['Temperature', 'Wind Speed']:\n",
    "        nsrdb = df.loc[df['dataset'] == 'NSRDB', var].values\n",
    "        wtk = df.loc[df['dataset'] == 'WTK', var].values\n",
    "        annual_stats.loc[group, var] = wilcoxon(nsrdb, wtk)[1]\n",
    "        \n",
    "annual_means = comp_ds.groupby(['dataset', 'year']).mean()\n",
    "for var in ['Temperature', 'Wind Speed']:\n",
    "    nsrdb = annual_means.loc['NSRDB'][var].values\n",
    "    wtk = annual_means.loc['WTK'][var].values\n",
    "    annual_stats.loc['Annual Means', var] = ttest_rel(nsrdb, wtk)[1]\n",
    "    \n",
    "annual_stats.index.name = 'year'\n",
    "annual_stats.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:57:16.369025Z",
     "start_time": "2019-02-07T02:57:16.161916Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GHI</th>\n",
       "      <th>DHI</th>\n",
       "      <th>DNI</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Wind Speed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monthly Means</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                GHI   DHI   DNI  Temperature  Wind Speed\n",
       "month                                                   \n",
       "1              0.00  0.01  0.00          0.0         0.0\n",
       "2              0.00  0.01  0.00          0.0         0.0\n",
       "3              0.00  0.00  0.00          0.0         0.0\n",
       "4              0.00  0.00  0.00          0.0         0.0\n",
       "5              0.00  0.00  0.00          0.0         0.0\n",
       "6              0.00  0.06  0.00          0.0         0.0\n",
       "7              0.00  0.00  0.00          0.0         0.0\n",
       "8              0.00  0.00  0.00          0.0         0.0\n",
       "9              0.00  0.00  0.00          0.0         0.0\n",
       "10             0.00  0.00  0.00          0.0         0.0\n",
       "11             0.00  0.72  0.00          0.0         0.0\n",
       "12             0.00  0.91  0.00          0.0         0.0\n",
       "Monthly Means  0.02  0.00  0.07          0.0         0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_stats = pd.DataFrame()\n",
    "groups = solar_comp.groupby('month')\n",
    "for group, df in groups:\n",
    "    for var in ['GHI', 'DHI', 'DNI']:\n",
    "        nsrdb = df.loc[df['dataset'] == 'NSRDB', var].values\n",
    "        wtk = df.loc[df['dataset'] == 'WTK', var].values\n",
    "        monthly_stats.loc[group, var] = wilcoxon(nsrdb, wtk)[1]\n",
    "\n",
    "monthly_means = solar_comp.groupby(['dataset', 'month']).mean()\n",
    "for var in ['GHI', 'DHI', 'DNI']:\n",
    "    nsrdb = monthly_means.loc['NSRDB'][var].values\n",
    "    wtk = monthly_means.loc['WTK'][var].values\n",
    "    monthly_stats.loc['Monthly Means', var] = ttest_rel(nsrdb, wtk)[1]\n",
    "\n",
    "\n",
    "groups = comp_ds.groupby('month')\n",
    "for group, df in groups:\n",
    "    for var in ['Temperature', 'Wind Speed']:\n",
    "        nsrdb = df.loc[df['dataset'] == 'NSRDB', var].values\n",
    "        wtk = df.loc[df['dataset'] == 'WTK', var].values\n",
    "        monthly_stats.loc[group, var] = wilcoxon(nsrdb, wtk)[1]\n",
    "        \n",
    "monthly_means = comp_ds.groupby(['dataset', 'month']).mean()\n",
    "for var in ['Temperature', 'Wind Speed']:\n",
    "    nsrdb = monthly_means.loc['NSRDB'][var].values\n",
    "    wtk = monthly_means.loc['WTK'][var].values\n",
    "    monthly_stats.loc['Monthly Means', var] = ttest_rel(nsrdb, wtk)[1]\n",
    "    \n",
    "monthly_stats.index.name = 'month'\n",
    "monthly_stats.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-02-07T02:57:16.666331Z",
     "start_time": "2019-02-07T02:57:16.370712Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GHI</th>\n",
       "      <th>DHI</th>\n",
       "      <th>DNI</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Wind Speed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diurnal Means</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                GHI   DHI   DNI  Temperature  Wind Speed\n",
       "hour                                                    \n",
       "0               NaN   NaN   NaN         0.00        0.00\n",
       "1               NaN   NaN   NaN         0.00        0.00\n",
       "2               NaN   NaN   NaN         0.00        0.00\n",
       "3               NaN   NaN   NaN         0.00        0.00\n",
       "4               NaN   NaN   NaN         0.00        0.00\n",
       "5               NaN   NaN   NaN         0.00        0.00\n",
       "6              0.00  0.00  0.00         0.00        0.11\n",
       "7              0.00  0.22  0.00         0.00        0.00\n",
       "8              0.00  0.00  0.00         0.00        0.00\n",
       "9              0.00  0.00  0.07         0.00        0.00\n",
       "10             0.00  0.00  0.00         0.00        0.00\n",
       "11             0.00  0.00  0.00         0.00        0.00\n",
       "12             0.00  0.00  0.00         0.00        0.00\n",
       "13             0.00  0.00  0.00         0.00        0.00\n",
       "14             0.00  0.00  0.00         0.00        0.09\n",
       "15             0.00  0.00  0.00         0.59        0.00\n",
       "16             0.00  0.00  0.00         0.00        0.00\n",
       "17             0.00  0.00  0.00         0.00        0.00\n",
       "18             0.00  0.00  0.00         0.00        0.00\n",
       "19             0.00  0.00  0.00         0.00        0.00\n",
       "20              NaN   NaN   NaN         0.00        0.00\n",
       "21              NaN   NaN   NaN         0.00        0.00\n",
       "22              NaN   NaN   NaN         0.00        0.00\n",
       "23              NaN   NaN   NaN         0.00        0.00\n",
       "Diurnal Means  0.02  0.00  0.32         0.00        0.03"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diurnal_stats = pd.DataFrame()\n",
    "groups = solar_comp.groupby('hour')\n",
    "for group, df in groups:\n",
    "    for var in ['GHI', 'DHI', 'DNI']:\n",
    "        nsrdb = df.loc[df['dataset'] == 'NSRDB', var].values\n",
    "        wtk = df.loc[df['dataset'] == 'WTK', var].values\n",
    "        diurnal_stats.loc[group, var] = wilcoxon(nsrdb, wtk)[1]\n",
    "\n",
    "groups = comp_ds.groupby('hour')\n",
    "for group, df in groups:\n",
    "    for var in ['Temperature', 'Wind Speed']:\n",
    "        nsrdb = df.loc[df['dataset'] == 'NSRDB', var].values\n",
    "        wtk = df.loc[df['dataset'] == 'WTK', var].values\n",
    "        diurnal_stats.loc[group, var] = wilcoxon(nsrdb, wtk)[1]\n",
    "        \n",
    "diurnal_stats = diurnal_stats.sort_index()\n",
    "        \n",
    "diurnal_means = solar_comp.groupby(['dataset', 'hour']).mean()\n",
    "for var in ['GHI', 'DHI', 'DNI']:\n",
    "    nsrdb = diurnal_means.loc['NSRDB'][var].values\n",
    "    wtk = diurnal_means.loc['WTK'][var].values\n",
    "    diurnal_stats.loc['Diurnal Means', var] = ttest_rel(nsrdb, wtk)[1]\n",
    "        \n",
    "diurnal_means = comp_ds.groupby(['dataset', 'hour']).mean()\n",
    "for var in ['Temperature', 'Wind Speed']:\n",
    "    nsrdb = diurnal_means.loc['NSRDB'][var].values\n",
    "    wtk = diurnal_means.loc['WTK'][var].values\n",
    "    diurnal_stats.loc['Diurnal Means', var] = ttest_rel(nsrdb, wtk)[1]\n",
    "    \n",
    "diurnal_stats.index.name = 'hour'\n",
    "diurnal_stats.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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